{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder for Anomaly Detection with scikit-learn, Keras and TensorFlow\n",
    "\n",
    "This script trains an autoencoder for anomaly detection. We use Python, scikit-learn, TensorFlow and Keras to prepare the data and train the model.\n",
    "\n",
    "The input data is sensor data. Here is one example:\n",
    "\n",
    "\"Time\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\",\"V20\",\"V21\",\"V22\",\"V23\",\"V24\",\"V25\",\"V26\",\"V27\",\"V28\",\"Amount\",\"Class\"\n",
    "\n",
    "0,-1.3598071336738,-0.0727811733098497,2.53634673796914,1.37815522427443,-0.338320769942518,0.462387777762292,0.239598554061257,0.0986979012610507,0.363786969611213,0.0907941719789316,-0.551599533260813,-0.617800855762348,-0.991389847235408,-0.311169353699879,1.46817697209427,-0.470400525259478,0.207971241929242,0.0257905801985591,0.403992960255733,0.251412098239705,-0.018306777944153,0.277837575558899,-0.110473910188767,0.0669280749146731,0.128539358273528,-0.189114843888824,0.133558376740387,-0.0210530534538215,149.62,\"0\"\n",
    "\n",
    "The autoencoder compresses the data and tries to reconstruct it. The reconstruction error finds anomalies.\n",
    "\n",
    "This Jupyter notebook was tested with Python 3.6.7, Numpy 1.16.4, scikit-learn 0.20.1 and tensorflow 2.0.0-alpha0. \n",
    "\n",
    "Kudos to David Ellison who created the foundation for this example: https://www.datascience.com/blog/fraud-detection-with-tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODOs:\n",
    "    \n",
    "Replace CSV data import with TF IO Kafka plugin... \n",
    "\n",
    "Open question: What are the steps to do this? \n",
    "(most of below code is for visualisation, not for training => Focus of this Notebook should be model ingestion and training - the visualisation is nice to have but can be neglected)\n",
    "\n",
    "\n",
    "### Code to change? \n",
    "This is the key line:\n",
    "df = pd.read_csv(\"../../data/sensor_data.csv\")\n",
    "\n",
    "=> Needs to be replaced with something like:\n",
    "kafka_sensordata = kafka_io.KafkaDataset(['sensor-topic'], group='xx', eof=True)\n",
    "\n",
    "=> Then we can build the Keras model and do training with the KafkaDataSet as parameter:\n",
    "\n",
    "model = tf.keras.Sequential([...])\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(kafka_sensordata, epochs=1, steps_per_epoch=1000, callbacks=[tensorboard_callback])\n",
    "\n",
    "### Optional: Visualization\n",
    "\n",
    "Optional (if easy to do): convert kafka input data into pandas dataframe (optional, just for visualisation)\n",
    "\n",
    "df = kafka_sensordata.???convertToPandasDataframe???\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "# matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_curve\n",
    "from sklearn.metrics import recall_score, classification_report, auc, roc_curve\n",
    "from sklearn.metrics import precision_recall_fscore_support, f1_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from pylab import rcParams\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input, Dense\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "#check TensorFlow verson: 1.x or 2.0?\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set random seed and percentage of test data\n",
    "RANDOM_SEED = 314 #used to help randomly select the data points\n",
    "TEST_PCT = 0.2 # 20% of the data\n",
    "\n",
    "#set up graphic style in this case I am using the color scheme from xkcd.com\n",
    "rcParams['figure.figsize'] = 14, 8.7 # Golden Mean\n",
    "LABELS = [\"Normal\",\"Fraud\"]\n",
    "#col_list = [\"cerulean\",\"scarlet\"]# https://xkcd.com/color/rgb/\n",
    "#sns.set(style='white', font_scale=1.75, palette=sns.xkcd_palette(col_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0     0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1     0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2     1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3     1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4     2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../../data/sensor_data.csv\") #unzip and read in data downloaded to the local directory\n",
    "df.head(n=5) #just to check you imported the dataset properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81708, 31)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #secondary check on the size of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any() #check to see if any values are null, which there are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    81509\n",
       "1      199\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['Class'], sort = True) #class comparison 0=Normal 1=Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#if you don't have an intuitive sense of how imbalanced these two classes are, let's go visual\n",
    "count_classes = pd.value_counts(df['Class'], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.title(\"Frequency by observation number\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Number of Observations\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_df = df[df.Class == 0] #save normal_df observations into a separate df\n",
    "fraud_df = df[df.Class == 1] #do the same for frauds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    81509.000000\n",
       "mean        98.011838\n",
       "std        269.315311\n",
       "min          0.000000\n",
       "25%          7.710000\n",
       "50%         26.990000\n",
       "75%         89.460000\n",
       "max      19656.530000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_df.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     199.000000\n",
       "mean      100.503166\n",
       "std       229.722752\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         7.520000\n",
       "75%        99.990000\n",
       "max      1809.680000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_df.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot of high value transactions\n",
    "bins = np.linspace(200, 2500, 100)\n",
    "plt.hist(normal_df.Amount, bins, alpha=1, normed=True, label='Normal')\n",
    "plt.hist(fraud_df.Amount, bins, alpha=0.6, normed=True, label='Fraud')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Amount by percentage of transactions (transactions \\$200+)\")\n",
    "plt.xlabel(\"Transaction amount (USD)\")\n",
    "plt.ylabel(\"Percentage of transactions (%)\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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nPvc5XvKSl/Ce97xn4O9jNAOUJEmSpFVuvCV8r3jFK9hkk00AqCre//7389Of/pR11lmH6667jt///vfjjnn77bdz22238ZKXvASAffbZh7POOmswb2AcBihJkiRJq82jH/3oB5+feOKJ3HTTTVxwwQVMmzaN2bNns2TJEqZOncqyZcsevG7JkiVAL3AlWe019/MeKEmSJElrxO23385jH/tYpk2bxo9+9COuvvpqAB73uMdx4403csstt3Dffffx7W9/G4CNN96YjTbaiHPOOQfoBbDVzRkoSZIkSWvE3nvvzbx585g7dy7bbLMNz3jGMwCYNm0ahx56KDvssANz5sx5sB3guOOO481vfjOPetSjeOUrX7naa05VrfYXXd3mzp1bCxYsWNNlSMNtZbY3bdjKVJIkrT5XXHEFz3zmM9d0GUNrrM8nyQVVNXeyvgNdwpdk5yRXJlmY5JAxzv9DksuTXJzkB0me1HduvyS/6R779bVvm+SSbszPZk0vgpQkSZK01hhYgEoyBTgS2AXYCtgzyVajLrsQmFtVzwFOAf6167sJ8CFgB2B74ENJHtP1+TxwILBl99h5UO9BkiRJkvoNcgZqe2BhVV1VVX8ETgZ27b+gqn5UVfd0hz8HZnbPXwl8r6purao/AN8Ddk7yBGDDqjq3emsPvwzsNsD3IEmSJEkPGmSA2hy4tu94cdc2ngOAkU3cx+u7efe8dUxJkiRprbQ27HWwIlb2cxlkgBrr3qQxq03yRmAu8H8n6bs8Yx6YZEGSBTfddFNDuZIkSdIjw/Tp07nlllsMUaNUFbfccgvTp09f4TEGuY35YmCLvuOZwPWjL0ryl8A/AS+pqvv6+v7FqL4/7tpnjmr/kzEBquoo4Cjo7cK3Im9AkiRJejiaOXMmixcvxomEPzV9+nRmzpw5+YXjGGSAOh/YMskc4DpgD2Cv/guSPA/4ArBzVd3Yd+ps4GN9G0fsBLyvqm5NcmeSFwDnAfsCnxvge5AkSZIedqZNm8acOXPWdBmPSAMLUFW1NMlB9MLQFODYqrosyeHAgqqaT2/J3p8B3+h2I7+mql7bBaV/phfCAA6vqlu7528DjgfWp3fP1FlIkiRJ0mowyBkoqupM4MxRbYf2Pf/LCfoeCxw7RvsC4NmrsExJkiRJajLQL9KVJEmSpEcSA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNRpogEqyc5IrkyxMcsgY51+c5JdJliZ5XV/7S5Nc1PdYkmS37tzxSX7Xd26bQb4HSZIkSRoxdVADJ5kCHAm8AlgMnJ9kflVd3nfZNcD+wLv7+1bVj4BtunE2ARYC3+275D1VdcqgapckSZKksQwsQAHbAwur6iqAJCcDuwIPBqiqWtSdWzbBOK8DzqqqewZXqiRJkiRNbpBL+DYHru07Xty1La89gK+OavtokouTfCbJemN1SnJgkgVJFtx0000r8LKSJEmS9FCDDFAZo62Wa4DkCcDWwNl9ze8DngFsB2wCvHesvlV1VFXNraq5M2bMWJ6XlSRJkqQxDTJALQa26DueCVy/nGO8Hji1qu4faaiqG6rnPuA4eksFJUmSJGngBhmgzge2TDInybr0luLNX84x9mTU8r1uVookAXYDLl0FtUqSJEnSpAYWoKpqKXAQveV3VwBfr6rLkhye5LUASbZLshjYHfhCkstG+ieZTW8G6yejhj4xySXAJcBmwEcG9R4kSZIkqd8gd+Gjqs4EzhzVdmjf8/PpLe0bq+8ixth0oqpetmqrlCRJkqQ2A/0iXUmSJEl6JDFASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNTJASZIkSVIjA5QkSZIkNRpogEqyc5IrkyxMcsgY51+c5JdJliZ53ahzDyS5qHvM72ufk+S8JL9J8rUk6w7yPUiSJEnSiIEFqCRTgCOBXYCtgD2TbDXqsmuA/YGTxhji3qrapnu8tq/9E8BnqmpL4A/AAau8eEmSJEkawyBnoLYHFlbVVVX1R+BkYNf+C6pqUVVdDCxrGTBJgJcBp3RNXwJ2W3UlS5IkSdL4BhmgNgeu7Tte3LW1mp5kQZKfJxkJSZsCt1XV0hUcU5IkSZJW2NQBjp0x2mo5+s+qquuTPBn4YZJLgDtax0xyIHAgwKxZs5bjZSVJkiRpbIOcgVoMbNF3PBO4vrVzVV3f/bwK+DHwPOBmYOMkI8Fv3DGr6qiqmltVc2fMmLH81UuSJEnSKIMMUOcDW3a75q0L7AHMn6QPAEkek2S97vlmwJ8Dl1dVAT8CRnbs2w84bZVXLkmSJEljGFiA6u5TOgg4G7gC+HpVXZbk8CSvBUiyXZLFwO7AF5Jc1nV/JrAgya/oBaaPV9Xl3bn3Av+QZCG9e6KOGdR7kCRJkqR+g7wHiqo6EzhzVNuhfc/Pp7cMb3S//wK2HmfMq+jt8CdJkiRJq9VAv0hXkiRJkh5JDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1GjqRCeTzAT2AP4P8ETgXuBS4AzgrKpaNvAKJUmSJGlIjBugkhwHbA58G/gEcCMwHXgasDPwT0kOqaqfro5CJUmSJGlNm2gG6lNVdekY7ZcC30qyLjBrMGVJkiRJ0vAZ9x6oscJTkqck2bo7/8eqWjjI4iRJkiRpmEx4D1S/JO8HtgaWJVlWVfsMrixJkiRJGj7jzkAl+fskU/qanltVe1bV3sBzB1+aJEmSJA2XibYx/wPwnSTzuuPvJvlJkv8POHvwpUmSJEnScJnoHqivAPOAbZKcBiwAdgFeU1XvWU31SZIkSdLQmOyLdJ8CfA34W+Ag4N+A9QddlCRJkiQNo4m+B+r47vz6wG+r6i1Jngd8MckvquqfV1ONkiRJkjQUJtqF73lV9VyAJBcCVNWFwLwku66O4iRJkiRpmEwUoL6T5CfAusBJ/Seq6rSBViVJkiRJQ2jcAFVV702yIbCsqu5ajTVJkiRJ0lCa6Hug3gjcNV54SvKUJC8aWGWSJEmSNGQmWsK3KXBhkguAC4CbgOnAU4GXADcDhwy8QkmSJEkaEhMt4Tsiyb8DLwP+HHgOcC9wBbBPVV2zekqUJEmSpOEw0QwUVfUA8L3uIUmSJElrtcm+SFeSJEmS1DFASZIkSVIjA5QkSZIkNZo0QCU5OMmG6TkmyS+T7LQ6ipMkSZKkYdIyA/XmqroD2AmYAbwJ+PhAq5IkSZKkIdQSoNL9fBVwXFX9qq9NkiRJktYaLQHqgiTfpRegzk6yAbBssGVJkiRJ0vCZ8HugOgcA2wBXVdU9STalt4xPkiRJktYqkwaoqlqW5PfAVklaApckSZIkPSJNGoiSfAJ4A3A58EDXXMBPB1iXJEmSJA2dlhml3YCnV9V9gy5GkiRJkoZZyyYSVwHTBl2IJEmSJA27lhmoe4CLkvwAeHAWqqreMbCqJEmSJGkItQSo+d1DkiRJktZqLbvwfSnJusDTuqYrq+r+wZYlSZIkScOnZRe+vwC+BCwCAmyRZL+qchc+SZIkSWuVliV8nwJ2qqorAZI8DfgqsO0gC5MkSZKkYdOyC9+0kfAEUFW/xl35JEmSJK2FWmagFiQ5BjihO94buGBwJUmSJEnScGqZgXobcBnwDuBg4HLgrS2DJ9k5yZVJFiY5ZIzzL07yyyRLk7yur32bJOcmuSzJxUne0Hfu+CS/S3JR99impRZJkiRJWlktu/DdB3y6ezRLMgU4EngFsBg4P8n8qrq877JrgP2Bd4/qfg+wb1X9JskTgQuSnF1Vt3Xn31NVpyxPPZIkSZK0ssYNUEm+XlWvT3IJUKPPV9VzJhl7e2BhVV3VjXcysCu9GayRMRZ155aNGvvXfc+vT3IjMAO4DUmSJElaQyaagTq4+/maFRx7c+DavuPFwA7LO0iS7YF1gd/2NX80yaHAD4BDulmy0f0OBA4EmDVr1vK+rCRJkiT9iXHvgaqqG7qnb6+qq/sfwNsbxs5Ywy5PcUmeQG/zijdV1cgs1fuAZwDbAZsA7x2n/qOqam5VzZ0xY8byvKwkSZIkjallE4lXjNG2S0O/xcAWfcczgetbigJIsiFwBvCBqvr5SHtV3VA99wHH0VsqKEmSJEkDN26ASvK27v6nZ3Q74Y08fgdc0jD2+cCWSeYkWRfYA5jfUlR3/anAl6vqG6POPaH7GWA34NKWMSVJkiRpZU10D9RJwFnAvwD9W5DfWVW3TjZwVS1NchBwNjAFOLaqLktyOLCgquYn2Y5eUHoMMC/Jh6vqWcDrgRcDmybZvxty/6q6CDgxyQx6SwQvonFLdUmSJElaWeMGqKq6Hbg9yRHArVV1J0CSDZLsUFXnTTZ4VZ0JnDmq7dC+5+fTW9o3ut9XgK+MM+bLJntdSZIkSRqElnugPg/c1Xd8d9cmSZIkSWuVlgCVqnpw97xuN7xJv4BXkiRJkh5pWgLUVUnekWRa9zgYuGrQhUmSJEnSsGkJUG8FdgSu43+/DPfAQRYlSZIkScNo0qV4VXUjvS3IJUmSJGmtNmmASjIdOAB4FjB9pL2q3jzAuiRJkiRp6LQs4TsBeDzwSuAn9LYdv3OQRUmSJEnSMGoJUE+tqg8Cd1fVl4BXA1sPtixJkiRJGj4tAer+7udtSZ4NbATMHlhFkiRJkjSkWr7P6agkjwE+AMwH/gw4dKBVSZIkSdIQatmF7+ju6U+BJw+2HEmSJEkaXpMu4UtycJIN03N0kl8m2Wl1FCdJkiRJw6TlHqg3V9UdwE7AY4E3AR8faFWSJEmSNIRaAlS6n68CjquqX/W1SZIkSdJaoyVAXZDku/QC1NlJNgCWDbYsSZIkSRo+LbvwHQBsA1xVVfck2ZTeMj5JkiRJWqu07MK3LMnvga2StAQuSZIkSXpEmjQQJfkE8AbgcuCBrrnobWsuSZIkSWuNlhml3YCnV9V9gy5GkiRJkoZZyyYSVwHTBl2IJEmSJA27lhmoe4CLkvwAeHAWqqreMbCqJEmSJGkItQSo+d1DkiRJktZqLbvwfWl1FCJJkiRJw65lF74tgX8BtgKmj7RX1ZMHWJckSZIkDZ2WTSSOAz4PLAVeCnwZOGGQRUmSJEnSMGoJUOtX1Q+AVNXVVXUY8LLBliVJkiRJw6dlE4klSdYBfpPkIOA64LGDLUuSJEmShk/LDNQ7gUcB7wC2Bd4I7DfIoiRJkiRpGE04A5VkCvD6qnoPcBfwptVSlSRJkiQNoQlnoKrqAWDbJFlN9UiSJEnS0Gq5B+pC4LQk3wDuHmmsqm8NrCpJkiRJGkItAWr++9eZAAAgAElEQVQT4BYeuvNeAQYoSZIkSWuVlgB1dFX9rL8hyZ8PqB5JkiRJGlotu/B9rrFNkiRJkh7Rxp2BSvJCYEdgRpJ/6Du1ITBl0IVJkiRJ0rCZaAnfusCfddds0Nd+B/C6QRYlSZIkScNo3ABVVT8BfpLk+Kq6ejXWJEmSJElDadJ7oAxPkiRJktTTsomEJEmSJIkJAlSST3Q/d1995UiSJEnS8JpoE4lXJfkA8D7gG6upHklryEnnXbPCffeatwoLkSRJGmITBajvADcDj05yBxCgRn5W1YaroT5JkiRJGhoT7cL3HuA9SU6rql1XY00alNMPXvG+845YdXVIkiRJD1MTzUABUFW7JnkcsF3XdF5V3TTYsiRJkiRp+EwaoLpNJD4J/Jje8r3PJXlPVZ0y4NokLYfZh5yxUv0/Nul/DSRJktSyjfkHgO2qar+q2hfYHvhgy+BJdk5yZZKFSQ4Z4/yLk/wyydIkrxt1br8kv+ke+/W1b5vkkm7MzyZJSy2SJEmStLJaAtQ6VXVj3/EtLf2STAGOBHYBtgL2TLLVqMuuAfYHThrVdxPgQ8AO9ALbh5I8pjv9eeBAYMvusXPDe5AkSZKkldayaOc7Sc4GvtodvwE4s6Hf9sDCqroKIMnJwK7A5SMXVNWi7tyyUX1fCXyvqm7tzn8P2DnJj4ENq+rcrv3LwG7AWQ31SJIkSdJKadlE4j1J/gp4Eb17oI6qqlMbxt4cuLbveDG9GaUWY/XdvHssHqNdkiRJkgau6bbxqvoW8K3lHHuse5NqJfs2j5nkQHpL/Zg1a1bjy0qSJEnS+FrugVpRi4Et+o5nAtevZN/F3fNJx6yqo6pqblXNnTFjRnPRkiRJkjSeQW5cfD6wZZI5wHXAHsBejX3PBj7Wt3HETsD7qurWJHcmeQFwHrAv8LlVXLekh4mV2bp90cdfvQorkSRJa4umAJVkfWBWVV3ZOnBVLU1yEL0wNAU4tqouS3I4sKCq5ifZDjgVeAwwL8mHq+pZXVD6Z3ohDODwkQ0lgLcBxwPr09s8wg0ktOqdfvCK9513xAp3XdnvctJyWEO/Y0mS9PDW8kW68+h9ke66wJwk29ALNK+drG9VncmoHfuq6tC+5+fz0CV5/dcdCxw7RvsC4NmTvbYkSZIkrWot90AdRm9L8tsAquoiYPbgSpIkSZKk4dQSoJZW1e0Dr0SSJEmShlzLPVCXJtkLmJJkS+AdwH8NtixJkiRJGj4tAervgX8C7gO+Sm9TiH8eZFGS1h5unCFJkh5OJg1QVXUPvQD1T4MvR5IkSZKGV8sufKcDNar5dmAB8IWqWjKIwiRJkiRp2LRsInEVcBfwxe5xB/B74GndsSRJkiStFVrugXpeVb247/j0JD+tqhcnuWxQhUmSJEnSsGmZgZqRZNbIQfd8s+7wjwOpSpIkSZKGUMsM1LuAc5L8FggwB3h7kkcDXxpkcZIkSZI0TFp24Tuz+/6nZ9ALUP/dt3HEvw2yOEmSJEkaJi0zUABbAk8HpgPPSUJVfXlwZUnSYJ103jUr3HeveauwEEmS9LDSso35h4C/ALYCzgR2Ac4BDFCSJEmS1iotm0i8Dng58D9V9SbgucB6A61KkiRJkoZQyxK+e6tqWZKlSTYEbgSePOC6JD2MzD7kjDVdgiRJ0mrREqAWJNmY3pfmXkDvS3V/MdCqJEmSJGkItezC9/bu6X8k+Q6wYVVdPNiyJEmSJGn4THoPVJIfjDyvqkVVdXF/myRJkiStLcadgUoyHXgUsFmSx9D7DiiADYEnrobaJEmSJGmoTLSE72+Bd9ILSxfwvwHqDuDIAdclSZIkSUNn3ABVVUcARyT5+6r63GqsSZIkSZKGUssmEp9LsiMwu//6qvKLdCVJkiStVSYNUElOAJ4CXAQ80DUXYICSJEmStFZp+R6oucBWVVWDLkaSJEmShtmk25gDlwKPH3QhkiRJkjTsWmagNgMuT/IL4L6Rxqp67cCqkiRJkqQh1BKgDht0EZIkSZL0cNCyC99PkjwJ2LKqvp/kUcCUwZcmSZIkScNl0nugkrwFOAX4Qte0OfCfgyxKkiRJkoZRyyYSfwf8OXAHQFX9BnjsIIuSJEmSpGHUEqDuq6o/jhwkmUrve6AkSZIkaa3SEqB+kuT9wPpJXgF8Azh9sGVJkiRJ0vBpCVCHADcBlwB/C5wJfGCQRUmSJEnSMGrZxnx94Niq+iJAkild2z2DLEySJEmShk3LDNQP6AWmEesD3x9MOZIkSZI0vFoC1PSqumvkoHv+qMGVJEmSJEnDqSVA3Z3k+SMHSbYF7h1cSZIkSZI0nFrugToY+EaS67vjJwBvGFxJkiRJkjScJgxQSdYB1gWeATwdCPDfVXX/aqhNkiRJkobKhAGqqpYl+VRVvRC4dDXVJEmSJElDqeUeqO8m+eskGXg1kiRJkjTEWu6B+gfg0cADSe6lt4yvqmrDgVYmSZIkSUNm0gBVVRusjkIkSZIkadhNuoQvPW9M8sHueIsk2w++NEmSJEkaLi33QP0/4IXAXt3xXcCRA6tIkiRJkoZUyz1QO1TV85NcCFBVf0iy7oDrkiRJkqSh0zIDdX+SKUABJJkBLGsZPMnOSa5MsjDJIWOcXy/J17rz5yWZ3bXvneSivseyJNt0537cjTly7rGN71WSJEmSVkpLgPoscCrw2CQfBc4BPjZZpy50HQnsAmwF7Jlkq1GXHQD8oaqeCnwG+ARAVZ1YVdtU1TbAPsCiqrqor9/eI+er6saG9yBJkiRJK61lF74Tk1wAvJzeFua7VdUVDWNvDyysqqsAkpwM7Apc3nfNrsBh3fNTgH9Pkqqqvmv2BL7a8HqSJEmSNFDjBqgk04G3Ak8FLgG+UFVLl2PszYFr+44XAzuMd01VLU1yO7ApcHPfNW+gF7T6HZfkAeCbwEdGBa6R+g8EDgSYNWvWcpQtSZIkSWObaAnfl4C59MLTLsAnl3PsjNE2OuhMeE2SHYB7qurSvvN7V9XWwP/pHvuM9eJVdVRVza2quTNmzFi+yiVJkiRpDBMt4duqCyokOQb4xXKOvRjYou94JnD9ONcsTjIV2Ai4te/8HoxavldV13U/70xyEr2lgl9eztokSZIkablNNAN1/8iT5Vy6N+J8YMskc7ptz/cA5o+6Zj6wX/f8dcAPR5bjJVkH2B04eeTiJFOTbNY9nwa8BrgUSZIkSVoNJpqBem6SO7rnAdbvjgNUVW040cDdPU0HAWcDU4Bjq+qyJIcDC6pqPnAMcEKShfRmnvboG+LFwOKRTSg66wFnd+FpCvB94Iutb1aSJEmSVsa4Aaqqpqzs4FV1JnDmqLZD+54voTfLNFbfHwMvGNV2N7DtytYlSZIkSSui5XugJEmSJEkYoCRJkiSpmQFKkiRJkhoZoCRJkiSpkQFKkiRJkhoZoCRJkiSp0UTfAyVpBcw+5Iw1XYIkSZIGxBkoSZIkSWrkDJQ0RD429egV7vv+pX+zCiuRJEnSWJyBkiRJkqRGBihJkiRJamSAkiRJkqRGBihJkiRJamSAkiRJkqRGBihJkiRJamSAkiRJkqRGBihJkiRJamSAkiRJkqRGU9d0AXqYOP3gFe8774hVV4ckSZK0BjkDJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1Gjqmi5A0trtY1OPXuG+71/6N6uwEkmSpMk5AyVJkiRJjQxQkiRJktTIACVJkiRJjQxQkiRJktTIACVJkiRJjQxQkiRJktTIACVJkiRJjQxQkiRJktTIACVJkiRJjQxQkiRJktRo6pouQJrQ6QeveN95R6y6OiRJkiScgZIkSZKkZgYoSZIkSWpkgJIkSZKkRgYoSZIkSWo00ACVZOckVyZZmOSQMc6vl+Rr3fnzkszu2mcnuTfJRd3jP/r6bJvkkq7PZ5NkkO9BkiRJkkYMLEAlmQIcCewCbAXsmWSrUZcdAPyhqp4KfAb4RN+531bVNt3jrX3tnwcOBLbsHjsP6j1IkiRJUr9BzkBtDyysqquq6o/AycCuo67ZFfhS9/wU4OUTzSgleQKwYVWdW1UFfBnYbdWXLkmSJEl/apABanPg2r7jxV3bmNdU1VLgdmDT7tycJBcm+UmS/9N3/eJJxpQkSZKkgRjkF+mONZNUjdfcAMyqqluSbAv8Z5JnNY7ZGzg5kN5SP2bNmtVctCRJkiSNZ5AzUIuBLfqOZwLXj3dNkqnARsCtVXVfVd0CUFUXAL8FntZdP3OSMen6HVVVc6tq7owZM1bB25EkSZK0thtkgDof2DLJnCTrAnsA80ddMx/Yr3v+OuCHVVVJZnSbUJDkyfQ2i7iqqm4A7kzygu5eqX2B0wb4HiRJkiTpQQNbwldVS5McBJwNTAGOrarLkhwOLKiq+cAxwAlJFgK30gtZAC8GDk+yFHgAeGtV3dqdextwPLA+cFb3eNiYfcgZK9x30cdfvQorkSRJkrS8BnkPFFV1JnDmqLZD+54vAXYfo983gW+OM+YC4NmrtlJJkiRJmtxAv0hXkiRJkh5JDFCSJEmS1MgAJUmSJEmNDFCSJEmS1Gigm0hID1cnnXfNmi5BkiRJQ8gZKEmSJElqZICSJEmSpEYu4ZO00j429ei16nUlSdLayxkoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWpkgJIkSZKkRgYoSZIkSWo0dU0XoLXA6Qev6QpWq49NPXpNlyBJkqQBcQZKkiRJkhoZoCRJkiSpkQFKkiRJkhoZoCRJkiSpkQFKkiRJkhoZoCRJkiSpkQFKkiRJkhoZoCRJkiSpkQFKkiRJkhoZoCRJkiSpkQFKkiRJkhoZoCRJkiSpkQFKkiRJkhoZoCRJkiSpkQFKkiRJkhpNXdMFqN3sQ85Yqf6L/nwVFSJJkiStpQY6A5Vk5yRXJlmY5JAxzq+X5Gvd+fOSzO7aX5HkgiSXdD9f1tfnx92YF3WPxw7yPUiSJEnSiIHNQCWZAhwJvAJYDJyfZH5VXd532QHAH6rqqUn2AD4BvAG4GZhXVdcneTZwNrB5X7+9q2rBoGqXJEmSpLEMcgZqe2BhVV1VVX8ETgZ2HXXNrsCXuuenAC9Pkqq6sKqu79ovA6YnWW+AtUqSJEnSpAYZoDYHru07XsxDZ5Eeck1VLQVuBzYddc1fAxdW1X19bcd1y/c+mCRjvXiSA5MsSLLgpptuWpn3IUmSJEnAYDeRGCvY1PJck+RZ9Jb17dR3fu+qui7JBsA3gX2AL//JIFVHAUcBzJ07d/Tram1w+sFrugJJkiQ9wgxyBmoxsEXf8Uzg+vGuSTIV2Ai4tTueCZwK7FtVvx3pUFXXdT/vBE6it1RQkiRJkgZukAHqfGDLJHOSrAvsAcwfdc18YL/u+euAH1ZVJdkYOAN4X1X9bOTiJFOTbNY9nwa8Brh0gO9BkiRJkh40sCV8VbU0yUH0dtCbAhxbVZclORxYUFXzgWOAE5IspDfztEfX/SDgqcAHk3ywa9sJuBs4uwtPU4DvA18c1HvQw9tJ512zpkuQJEnSI8xAv0i3qs4EzhzVdmjf8yXA7mP0+wjwkXGG3XZV1ihJkiRJrQb6RbqSJEmS9Egy0BkoDZeVWdK21w6zVmElkiRJ0sOTAUpN1lT48j4mSZIkDROX8EmSJElSIwOUJEmSJDUyQEmSJElSI++Bkh4hPjb16DVdgiRJ0iOeM1CSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1MgAJUmSJEmNDFCSJEmS1Gjqmi5Aj3wnnXfNmi5BkiRJ+v/bu/t4u6Y7j+Ofr3tpUBJCPAURwkhbUmMig1E0NB5aqrxEmTF9MV4tXmXKdFKMqhcp1cG8ZjyMp/GQekw9BOMhREWoREgiEYxQU5eS8RBPY2j4zR9rHdk5Oefefe49515Xv+/X67zO2mvvvdba+6x9zl577bVPU7gB1csmtl/W10UwMzMzM7Nu8i18ZmZmZmZmJbkBZWZmZmZmVpIbUGZmZmZmZiW5AWVmZmZmZlaSG1BmZmZmZmYluQFlZmZmZmZWkhtQZmZmZmZmJfl/oMzMGjRswp3dXvfFs/ZpYknMzMyst7kHyszMzMzMrCQ3oMzMzMzMzEryLXxmZg2a2H5ZD9b2LXxmZmb9mXugzMzMzMzMSnIDyszMzMzMrCQ3oMzMzMzMzEpyA8rMzMzMzKwkN6DMzMzMzMxKcgPKzMzMzMyspJY2oCSNk/SspEWSJtSY/wVJN+T5MyUNK8z7SY5/VtI3yqZpZmZmZmbWKi1rQElqAy4A9gJGAodIGlm12BHAWxGxBXAecHZedyQwHvgSMA64UFJbyTTNzMzMzMxaopU9UKOBRRHxQkR8BFwP7Fe1zH7AVTk8Gfi6JOX46yPiw4j4HbAop1cmTTMzMzMzs5ZoZQNqI+ClwnRHjqu5TEQsBd4GBneybpk0zczMzMzMWqK9hWmrRlyUXKZefK0GX3WaKWHpKOCoPPmepGfrlLO3rQO83teFsM8N16d+5tAza329fWa4PlkzuT5ZM7k+WTPVq0+bllm5lQ2oDmDjwvRQ4JU6y3RIagcGAm92sW5XaQIQEZcAl3S38K0iaXZEbN/X5bDPB9cnaybXJ2sm1ydrJtcna6ae1qdW3sL3GDBC0maSViE9FGJK1TJTgMNz+EBgWkREjh+fn9K3GTACmFUyTTMzMzMzs5ZoWQ9URCyVdCxwD9AGXBERT0k6HZgdEVOAy4FrJC0i9TyNz+s+JelGYCGwFDgmIj4GqJVmq7bBzMzMzMysSKnDx3qLpKPy7YVmPeb6ZM3k+mTN5PpkzeT6ZM3U0/rkBpSZmZmZmVlJrRwDZWZmZmZm9rniBlQvkjRO0rOSFkma0Nflsf5F0hWSFktaUIhbW9JUSc/l97X6sozWf0jaWNIDkp6W9JSk43K865Q1TNIASbMkzcv16Wc5fjNJM3N9uiE/AMqsS5LaJM2RdEeedl2ybpP0oqT5kuZKmp3juv175wZUL5HUBlwA7AWMBA6RNLJvS2X9zJXAuKq4CcD9ETECuD9Pm5WxFDghIrYGxgDH5O8k1ynrjg+B3SNiW2AUME7SGOBs4Lxcn94CjujDMlr/chzwdGHadcl6areIGFV4fHm3f+/cgOo9o4FFEfFCRHwEXA/s18dlsn4kIqaTnlZZtB9wVQ5fBezfq4Wyfisi/hART+Twu6QTlY1wnbJuiOS9PLlyfgWwOzA5x7s+WSmShgL7AJflaeG6ZM3X7d87N6B6z0bAS4Xpjhxn1hPrRcQfIJ0QA0P6uDzWD0kaBnwVmInrlHVTvuVqLrAYmAo8DyyJiKV5Ef/uWVnnAz8GPsnTg3Fdsp4J4F5Jj0s6Ksd1+/euZf8DZStQjTg/AtHM+pSkLwK/Bo6PiHfShV6zxuX/axwlaRBwC7B1rcV6t1TW30jaF1gcEY9L2rUSXWNR1yVrxE4R8YqkIcBUSc/0JDH3QPWeDmDjwvRQ4JU+Kot9frwmaQOA/L64j8tj/YiklUmNp19FxM052nXKeiQilgC/IY2tGySpcrHWv3tWxk7AtyS9SBrusDupR8p1ybotIl7J74tJF3hG04PfOzeges9jwIj8FJlVgPHAlD4uk/V/U4DDc/hw4LY+LIv1I3lMweXA0xFxbmGW65Q1TNK6uecJSasCY0nj6h4ADsyLuT5ZlyLiJxExNCKGkc6VpkXEobguWTdJWl3SGpUwsCewgB783vmPdHuRpL1JV1HagCsi4sw+LpL1I5KuA3YF1gFeA34K3ArcCGwC/B44KCKqHzRhtgJJOwMPAfNZNs7gJNI4KNcpa4ikbUiDsNtIF2dvjIjTJQ0n9SKsDcwBDouID/uupNaf5Fv4ToyIfV2XrLty3bklT7YD10bEmZIG083fOzegzMzMzMzMSvItfGZmZmZmZiW5AWVmZmZmZlaSG1BmZmZmZmYluQFlZmZmZmZWkhtQZmZmZmZmJbkBZWZmZmZmVpIbUGa2HEmDJc3Nr1clvVyYXuUzUL4DJP1ZYfpMSbs1Id3tJI0rTH9b0j/0NF1LJHVU/mi1Kn68pKcl3dfi/FeVNC3X4wMl/YekrRpYf7l6V3ZeX5E0XNL4wvQOks5rUtr3VP6UsuTy1cfsDEmjmlSWMyQd30j+Tcp3uf1bY/5QSbfl8JGSzm9m/l2UbX1Jd/ZWfmZ/itr7ugBm9tkSEW8AowAknQa8FxG/LC4jSaT/kftkxRRa7gDSH78+AxARJzcp3e2ALwN353Rv6Xxxk9QeEUt7mMyRwFER8VAL0i76cyAionLiPrnWQpLaIuLjGrOWq3dl57VgO8oaDown/fEoETGT9CfJPRYR32hwlc72XW9oRf7L7d8aTgAuaWJ+K6hXtyLiVUlvStohf+5m1mTugTKzUiRtIWmBpIuBJ4ANJF0iabakpySdWli2Q9JpkuZIelLSljl+d0nzci/AE5JWl7Rm7hl4Ii+7byGd7+W4ebnH4K+AvYHzchrDJE2StH9efo8cP1/SpZUes3rlKeSzKnAqcGihh+LTq8Y5jwskPSDpeUm7SLpK0jOSLi+ks5ek3+ZtuUHS6jX24/clPZa36aacd6UnZkGOf6DGemNz/pMlPZevvP9NTutJScPycutJujl/LrMkjcnxY3LZ5kh6WNKIHP+VnMbcnM7w/FnPLeQ9QdIpOTxDqddvOnBsJ/mtK2lq3hcXAaqxTacDY4DLJJ2V9/n1ku4A7pK0kqRz836ZL+nARvZFIZ8NgSuB7Qv1ZoakUZLaJS3JacwCRks6R9LCnNbZtepdIe1adbJ6H+0naWbe9/dKGpLXPUPS5ZIelPSCpGNy/BqS7sp1YUFhu3+Wt3GBpIslKcdvqXQMzcv7exhwFrBbLtMP8z67NS+/jqQpefsekfTlzspT43PrkDRIy74TLlf6DrhL0oCqZevtu/G5vjwrace8bHv+vGflsh1ZJ/9T83pTgRGF+BWOrTqfT+ljsJMyLbd/q8onYH9gaiF6qFLP3XOSfl5Y9jClur1A0sRCnksKy4yXdFkOT5L0z7l8E1XjOzWvditwaK39Z2ZNEBF++eWXXzVfwGnAiTm8Bekq7l8U5q+d39uBh4CReboD+EEO/xC4OIfvAnbI4S8CbcDKwBo5bgjwXA5vS7pivHZVXpOA/QtlmEQ6WVkNeAnYPMf/Cji2s/JUbeuRwPm1pnMek3L4O8DbwEjSRai5pJ6rIcCDwGp5uZOBk2rkM7gQPqtQrqeB9XJ4UI31xgJvAusBA4BXgVPzvBOAX+bwDcCYHB4GLMjhgUBbDo8Dbsjhi4CDc/gLOe0tgLmFvCcAp+TwDOBfC/Pq5XdhZfuB/YCos10zgFGFff7fwFp5+mBSj2AbsH7+fIeU3Rc19t+t1fmS6m4AB+T49YCnSD2sn34WVNW7qrSr62T1PlqrkN73gbNz+AzScbNK3q438rYeDFxUWH9g1TEg4Dpgrzz9OPDNHB5AOhaqt/fT6fyZn5zDewKzOytPje3tAAblevJH4Cs5/mZgfMn9U9kH3wLuzuGjgQmFujgH2KQqrdHAPGBVUp3+HXB8F8dWdf6lj8F6Zarev1VlHAHMrPoueQ5YI5f7JWBDYCjwIrAO6XvwQWBfUp1cUlh/PHBZYVtuBVaq952aw5sCc2qVzy+//Or5y7fwmVkjno+IxwrTh0g6gvSDvyGpUbEwz7s5vz9OugIM8DBwvqRrgV9HxHuS2oCzJe1MaqBtLGkdYHfSSf6bAJX3TmxNanw9n6evBo4A/q2T8jTi9vw+H3glIhYCSFpIajhsQdr+R3LHwCqkE8Vq2yj1vAwinVDdkeMfBq6WdFOhrNVmRsRrOd8XgHsKZfrLHB4LbJXLALBWvsI+KKe/eVWajwCnSNoUuDkiFhXWrad421K9/HYh7+eIuE3Su10lmt0bEW/l8M7AtZFuqXtV0gxge+Ajyu2Lsj4CKrdsvkmqh5cqjSO5o+5anSvuo02AGyWtTzoJ/6/CvDsi4iNgsaQ3gXWBJ4GzJJ0F3B4RD+dlv640Lm8A6aT7cUmPAutExO0AEfF/AF18hjsD++Tl75V0ZaHnolZ5Xu0krUURMT+HHycdC2UUj8fKOnsCW2vZ2KKBpMbI7wvr7UL67vgA+EDS7YV59Y6tao0cg/XK1JkNgP+pirsvIt4FkPQMqU5sBEyLiNdz/LV5++7uIv2bYtnt0yt8p+b4xaTvZDNrATegzKwR71cCSreAHQeMjoglkiaRTuwqPszvH5O/ayLiDElTSCdvj0naFfga6aRku4hYKqkjpyNSz0BZXZ31r1CeBlXW/6QQrky35/zvjoi/7iKdq0k9Bwvy7UBjcvzfATuQrkDPk7RNoSFRXYbqclTKQC7H6HwS/ClJZwL3RMSFkrZg2VivayT9lvSZTJV0OOmEtXiL9wCgONbi/UK4Xn7Q2OdXL+16yuyLsj6IiACIiD9K2h7Yg3Tl/wekk+hGFbfjAmBiRPynpLGkHr2K4nZ8DLRHxNO5DHsD5yjd0ng+6WLAdhHxsqQzWHa8Nbqfq/drcXqF8nSRVqPLV69XXEfA0RFxfxfr1tveesdW2eVWOAbrlSl/jvV8wPLfhVB7P9Wr359UzatO69O6Ves7NccLQ7AAAAMvSURBVCKey+t80EkZzawHPAbKzLprTeBd4B1JGwBdDiyXtHlEPBkRPyfdCrMVqfG0ODee9iBdlQW4jzROYu287to5/l3SVeNqC4ERkobn6cNIt8SUVS/dsh4BvlbJX2l8V60r1auTelNWBr5biB8eEY8C/wS8xbL90Kj7gE/HrmjZ084GAi/n8N8W5g+PiEUR8S/AncA2pB6HDSWtpTSmZZ9u5DedPAZD0jfp3r6dTqoDbZLWA3YCZncjndKUni63ZkTcAfw98NU8q7P60VXdGQi8nMfGHF6iDBuRHt5yDXAu6QEnq5JOrF/PZfwOQG5kv573MZIGSFqtizIVP5uxQEdEvF9n2WYoe2zdAxwtqT2XbavK+KSC6cABeTvXJDV2KuodW9X5N3IM1itTZ9v0LLBZie19lDSOanBOfzzwYO5dekvSCEkrAd+ul0Cd71SALYEFJcpgZt3gBpSZddcTpEbLAuBS0q0kXTkxD5Z+ElgC3AtcA+woaTZwEGmsABHxJPALYLrSAw3OyWlcB5ykqsH8EfG/pFv2bpY0n3TF99IGtmcasK3SQP8DG1ivkv9rOf8bJM0jNai2rLHoqcAs0gDzhYX483K555Nu9+nuyc8xwE5KA94Xkq6qA5xN6s2o/py+q/QAgLmkJ4tNyreBTQQeA6ZUlbNsfj8Fxkp6AtiVZY23RkwmjYObR2qo/SgiFncjnUYMBO7Mn+E04Ec5vma9KzEP0ljCW0gN+tdKlGFbUm/CXODHpN6rN4CrSMfbLSz/RL1DgRPycTWDdNvdHKBN6QEDyz3kgFQHd8zLnw58r0SZeqKr/VPx76Tjf66kBaSxWsv1aEXELNL2zwNuIjWoKuodW9X5N3IM1itT3f0bEe8AL0nqtBEVER25LL8hjaV8NCIqjx//R1Iv8f2kMWf11PpOBdiNdEHEzFqgMqjVzMzMzJpA0kHAlyLitD7IW6SHgewTEW/3dv5mfwo8BsrMzMysuSaTejP7whDgF248mbWOe6DMzMzMzMxK8hgoMzMzMzOzktyAMjMzMzMzK8kNKDMzMzMzs5LcgDIzMzMzMyvJDSgzMzMzM7OS/h/LFuXOEPQYTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 48, 48) #48 hours\n",
    "plt.hist((normal_df.Time/(60*60)), bins, alpha=1, normed=True, label='Normal')\n",
    "plt.hist((fraud_df.Time/(60*60)), bins, alpha=0.6, normed=True, label='Fraud')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Percentage of transactions by hour\")\n",
    "plt.xlabel(\"Transaction time as measured from first transaction in the dataset (hours)\")\n",
    "plt.ylabel(\"Percentage of transactions (%)\");\n",
    "#plt.hist((df.Time/(60*60)),bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter((normal_df.Time/(60*60)), normal_df.Amount, alpha=0.6, label='Normal')\n",
    "plt.scatter((fraud_df.Time/(60*60)), fraud_df.Amount, alpha=0.9, label='Fraud')\n",
    "plt.title(\"Amount of transaction by hour\")\n",
    "plt.xlabel(\"Transaction time as measured from first transaction in the dataset (hours)\")\n",
    "plt.ylabel('Amount (USD)')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "#data = df.drop(['Time'], axis=1) #if you think the var is unimportant\n",
    "df_norm = df\n",
    "df_norm['Time'] = StandardScaler().fit_transform(df_norm['Time'].values.reshape(-1, 1))\n",
    "df_norm['Amount'] = StandardScaler().fit_transform(df_norm['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x = train_test_split(df_norm, test_size=TEST_PCT, random_state=RANDOM_SEED)\n",
    "train_x = train_x[train_x.Class == 0] #where normal transactions\n",
    "train_x = train_x.drop(['Class'], axis=1) #drop the class column (as Autoencoder is unsupervised and does not need / use labels for training)\n",
    "\n",
    "# test_x (without class) for validation; test_y (with Class) for prediction + calculating MSE / reconstruction error\n",
    "test_y = test_x['Class'] #save the class column for the test set\n",
    "test_x = test_x.drop(['Class'], axis=1) #drop the class column\n",
    "\n",
    "train_x = train_x.values #transform to ndarray\n",
    "test_x = test_x.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65209, 30)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce number of epochs and batch_size if your Jupyter crashes (due to memory issues)\n",
    "# or if you just want to demo and not run it for so long.\n",
    "# nb_epoch = 100 instead of 5\n",
    "# batch_size = 128 instead of 32\n",
    "nb_epoch = 5\n",
    "batch_size = 32\n",
    "\n",
    "\n",
    "# Autoencoder: 30 => 14 => 7 => 7 => 14 => 30 dimensions\n",
    "input_dim = train_x.shape[1] #num of columns, 30\n",
    "encoding_dim = 14\n",
    "hidden_dim = int(encoding_dim / 2) #i.e. 7\n",
    "learning_rate = 1e-7\n",
    "\n",
    "# Dense = fully connected layer\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "# First parameter is output units (14 then 7 then 7 then 30) :\n",
    "encoder = Dense(encoding_dim, activation=\"tanh\", activity_regularizer=regularizers.l1(learning_rate))(input_layer)\n",
    "encoder = Dense(hidden_dim, activation=\"relu\")(encoder)\n",
    "decoder = Dense(hidden_dim, activation='tanh')(encoder)\n",
    "decoder = Dense(input_dim, activation='relu')(decoder)\n",
    "autoencoder = Model(inputs=input_layer, outputs=decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 65209 samples, validate on 16342 samples\n",
      "Epoch 1/5\n",
      "65209/65209 [==============================] - 7s 110us/sample - loss: 0.8836 - accuracy: 0.3855 - val_loss: 0.9316 - val_accuracy: 0.4843\n",
      "Epoch 2/5\n",
      "65209/65209 [==============================] - 5s 84us/sample - loss: 0.7968 - accuracy: 0.5410 - val_loss: 0.8889 - val_accuracy: 0.5633\n",
      "Epoch 3/5\n",
      "65209/65209 [==============================] - 5s 75us/sample - loss: 0.7698 - accuracy: 0.5803 - val_loss: 0.8640 - val_accuracy: 0.5909\n",
      "Epoch 4/5\n",
      "65209/65209 [==============================] - 5s 77us/sample - loss: 0.7474 - accuracy: 0.5981 - val_loss: 0.8447 - val_accuracy: 0.6111\n",
      "Epoch 5/5\n",
      "65209/65209 [==============================] - 5s 75us/sample - loss: 0.7348 - accuracy: 0.6123 - val_loss: 0.8327 - val_accuracy: 0.6152\n"
     ]
    }
   ],
   "source": [
    "autoencoder.compile(metrics=['accuracy'],\n",
    "                    loss='mean_squared_error',\n",
    "                    optimizer='adam')\n",
    "\n",
    "cp = ModelCheckpoint(filepath=\"../../models/autoencoder_sensor_anomaly_detection_fully_trained_100_epochs.h5\",\n",
    "                               save_best_only=True,\n",
    "                               verbose=0)\n",
    "\n",
    "tb = TensorBoard(log_dir='./logs',\n",
    "                histogram_freq=0,\n",
    "                write_graph=True,\n",
    "                write_images=True)\n",
    "\n",
    "history = autoencoder.fit(train_x, train_x, # Autoencoder => Input == Output dimensions!\n",
    "                    epochs=nb_epoch,\n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=True,\n",
    "                    validation_data=(test_x, test_x), # Autoencoder => Input == Output dimensions!\n",
    "                    verbose=1,\n",
    "                    callbacks=[cp, tb]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "autoencoder = load_model('../../models/autoencoder_sensor_anomaly_detection.h5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/EfxlXlcoIiIiIlKjFKDk+3U+F6a8D+NfgKSecHAHvH6L07Vv/ZsQBq3wRURERERAAUoqyxjo9UO4ZSlc9gQktofsDJh/Hcw+H7Z86HWFIiIiIiLVTgFKTk5EJPSfALethLH/gPjmsHsVPHcpzP0RZOrAYhERERGpuxSg5NRExcDQm+HOdLjgPohtBNs+gScvgHlXw971XlcoIiIiIlLlFKDk9MTEw9l3wZ1r4Ky7ILo+ZLwNj4+A/06F3G1eVygiIiIiUmUUoKRqxDWGUffBHekwdCpERMHal+GxIfD2z6Fgj9cVioiIiIicNgUoqVoJLWDs3+H2NOg/Efw+WPGkc4bUe/fBkQNeVygiIiIicsoUoKR6NO4Il82EW1Oh5w/BdwQ+fxge7g+f/AOKD3ldoYiIiIjISVOAkurVvBdMeAGmfOCcJ1WcBx88ADMGwBdPgK/Y6wpFRERERCpNAUpqRtvBcP0bcP2b0GYwHN4Pi34JjyZD+ovgL/O6QhERERGR76UAJTWr80iY8j5MeBGSekHeDnj9FvhPCqx/E6z1ukIRERERkRNSgJKaZwz0HAe3fA6XPQGJHSA7A+ZfB7PPgy0fKEiJiIiISEhSgBLvRERC/wlwWxqM/Qc0aAG7V8Nzl8HcH8HOFV5XKCIiIiJSjgKUeC8qBobeDHeshlG/h9hG8M2n8NQomDcR9q73ukIREREREUABSkJJTDyc9TO4cy2c/XOIrg8ZC+HxEfDfqZC7zesKRURERCTMKUBJ6IlLhAv+D+5Ih6HTICIK1r4MjyXDW3dBwR6vKxQRERGRMKUAJaEroQWM/RvcvhL6Xw3WD2lPwSMD4L37oDDX6wpFREREJMwoQEnoa9wBLnscbkmFXj8C3xH4/GEnSH3ydyg+5HWFIiIiIhImFKCk9mjeE8Y/Dzd/AJ3Pg+I8+OCPMGMALJsJvmKvKxQRERGROk4BSmqfNoPh+tdh0v+gTTIc3g/v/AoeTYbVL4C/zOsKRURERKSOUoCS2qvTOTBlCUx4EZJ6Qd4OeONW+E8KrH9Dh/GKiIiISJVTgJLazRjoOQ5u+RwumwWJHSA7A+ZfD7PPgy0fKEiJiIiISJVRgJK6ISIS+o+H29Jg3D+hQQvYvRqeuwzm/gh2rvC6QhERERGpAxSgpG6JioEhU5wzpEb9HmIT4ZtP4alRMG8i7P3K6wpFREREpBZTgJK6KaY+nPUzuHMNnH03RNeHjIXw+Jnw6s2Qu9XrCkVERESkFlKAkrotLhEu+J0TpIZNh8ho+HI+PDYE3voZ5Gd5XaGIiIiI1CIKUBIeGjSHMX+F21fCgGvA+iFtDswYCO/9HxTmel2hiIiIiNQCClASXhLbw6X/gVtSodfF4DsCnz8Cj/SHj/8OxYe8rlBEREREQpgClISn5j1h/HNw84fQ+TwozocP/wgzBsCymeAr9rpCEREREQlBClAS3toMgutfh0n/g7ZD4PB+eOdX8OhgWP08lPm8rlBEREREQogClAhAp3Ng8nswYR407w15O+GNn8DjKfDV6zqMV0REREQABSiRIGOg51iY/hn8eDY07gjZG2HBJJh1Lmx+X0FKREREJMwpQIlUFBEJ/a6Cn6yAcf+CBi0hKx2e/zE880PYudzrCkVERETEIwpQIicSFQNDJsMdq2HUHyA2EbZ/Bk9dCC9OgL1feV2hiIiIiNQwBSiR7xNTH876qXMY7zm/gOh42LgIHj8TXp0CuVu9rlBEREREaogClEhlxSXC+ffCnekwbDpERsOXC+CxIfDWzyA/y+sKRURERKSaKUCJnKwGzWHMX+H2lTDgWrB+SJvjnCH17u+gMNfrCkVERESkmihAiZyqxPZw6b/h1mXQ62LwFcHSGfBIf/j4b1B8yOsKRURERKSKKUCJnK6kHjD+Obj5Q+hyPhTnw4d/coLUssehtMjrCkVERESkiihAiVSVNoPgutdg0lvQdggUZsM798Cjg2HVc1Dm87pCERERETlN1RqgjDGjjTEZxpjNxph7jjPewRjzvjFmrTHmI2NMW9fYJGPMpsBrkuvzwcaYLwPPnGGMMdX5HUROWqezYfJ7MPElaN4H8jPhzdvg8RT46nXw+72uUEREREROUbUFKGNMJPBvYAzQG5hojOld4bJ/AM9aa/sB9wN/CdzbBLgPGAYMBe4zxjQO3PM4MBXoFniNrq7vIHLKjIEeY2D6Z/Dj2dC4I2RvhAWTYPa5sHkJWOt1lSIiIiJykqpzBmoosNlau9VaWwK8BFxS4ZrewPuBnz90jf8AeM9am2utPQC8B4w2xrQCGlprU621FngWuLQav4PI6YmIgH5XwW1pMO5f0KAlZK2B5y+HZ34IO77wukIREREROQnVGaDaADtd7zMDn7mtAS4P/HwZkGCMafod97YJ/PxdzwTAGDPVGJNmjEnbv3//KX8JkSoRGQ1DJsMdq+HC+yE2EbZ/BnMughfHw551XlcoIiIiIpVQnQHqeHuTKq5ZuhsYaYxZDYwEdgG+77i3Ms90PrR2lrU22VqbnJSUVPmqRapTTH0480746Vo45xcQHQ8b34GZZ8GrUyBni9cVioiIiMh3qM4AlQm0c71vC+x2X2Ct3W2t/bG1diDw28Bned9xb2bg5xM+U6RWiG0E598Ld66BYbc4M1RfLoB/D4X//RTy9T9rERERkVBUnQFqBdDNGNPJGBMDTADedF9gjGlmjDlaw6+BOYGfFwMXGWMaB5pHXAQsttZmAQXGmOGB7nvXA29U43cQqV4NkmDMg3D7Shh4LVg/rHwaZgyEd++FwlyvKxQRERERl2oLUNZaH3AbThjaAMy31n5ljLnfGHNx4LJzgQxjzEagBfCnwL25wAM4IWwFcH/gM4BbgCeBzcAWYFF1fQeRGpPYHi75N9z6BfS+BHxFsPRR5zDej/8GxQVeVygiIiIigLFh0Eo5OTnZpqWleV2GSOXtXg3vPwBbAk0q6zeDs38OyTdBdKy3tYmIiIjUQcaYldba5O+7rloP0hWRU9R6IFz3X7jhbWg7FAqzYfGv4dHBsOpZKPN5XaGIiIhIWFKAEgllHc+Cye/CxJehxRmQnwlv3g7/GQ5fvQZ+v9cVioiIiIQVBSiRUGcM9BgN0z6FHz8JjTtBziZYcAPMPhc2LYEwWIorIiIiEgoUoERqi4gI6Hcl3LYCfvgQJLSCrDXwwuXwzDjY8YXXFYqIiIjUeQpQIrVNZLTTTOKO1XDhAxDXGLZ/DnMugheugj1fel2hiIiISJ2lACVSW0XHwZl3OIfxnvNLiI6HTYth5lnwymTI2eJ1hSIiIiJ1jgKUSG0X2wjO/60TpIbfCpExsO4VeGwI/O9OyN/tdYUiIiIidYYClEhd0SAJRv8Fbl8FA68DLKx8BmYMhHfvhcLc73uCiIiIiHwPBSiRuiaxHVzyGPxkOfS+FHxFsPRReLgffPRXKC7wukIRERGRWksBSqSuatYNrpoLUz+CrqOgpAA++jM80h9S/w2lRV5XKCIiIlLrKECJ1HWtB8K1r8INC6HdMCjMgcW/gUcHw6pnoczndYUiIiIitYYClEi46Hgm3LQYrp4PLc6A/Ex483b4zzBY91/w+72uUERERCTkKUCJhBNjoPsPYNqncPlT0LgT5GyGV26EWSNh03tgrddVioiIiIQsBSiRcBQRAX2vgNtWwA8fhoRWsGctvHAFPD0WdizzukIRERGRkKQAJRLOIqMh+Ua4YzVc+ADENYYdS2HOD+CFKyFrrdcVioiIiIQUBSgRgeg4OPMO5zDekb+CmAaw6V144mx45SbI2eJ1hSIiIiIhQQFKRIJiG8F5v3GC1PBbITIG1r0Kjw2BN++AvF1eVygiIiLiKQUoETlWfDMY/Re4fRUMvA6wsGouzBgIi38LBXu9rlBERETEE8aGQcet5ORkm5aW5nUZIrVX9ib48E/w1WuBDwy0Gwo9xkCPcc6hvcZ4WqKIiIjI6TDGrLTWJn/vdQpQIlJpu9Ph47/B5vegrCT4eZMugTA11jmsNzLKuxpFREREToEClIsClEgVKy6ALR9CxiLY+A4cyQ2OxTWGbj9wAlXXC6Begnd1ioiIiFSSApSLApRINfKXwc4vIGMhfL0Qcl0d+yJjoNM5TpjqPgYatfGuThEREZHvoADlogAlUoOyNzlhKmNR4EBe158xrfo7y/x6jIWWfbVvSkREREKGApSLApSIRw5nw8bFTqDa8gGUFgbHGrYN7JsaAx3Pgqh63tUpIiIiYU8BykUBSiQElBbBtk+Cs1OH9gTHYhKc/VI9xkK3C6F+E+/qFBERkbCkAOWiACUSYvx+yFrtBKmMRbB3XXDMREKHEcHZqSadvatTREREwoYClIsClEiIO7Dd6eaXsRC++Qz8vuBYUs9gi/Q2yRCh879FRESk6ilAuShAidQiRw7C5iXOzNSm96A4LzgWnwTdRzthqvO5EFPfqypFRESkjlGAcgmlAFXsK6NeVKTXZYjUDmWlsH1pYKnf23BwR3AsKhY6nxdokT4aElp4V6eIiIjUegpQLqESoN5I38W/3tvIizcPp01inNfliNQu1sK+9cEmFLtWugYNtE0OLvVL6qkW6SIiInJSFKBcQiFA+f2WCbOWsfybXDo3i2f+9BSaNVDbZpFTVrAnsG9qEWz9CHxFwbHGHQPnTY2B9ikQGe1VlSIiIlJLKEC5hEKAAsg7UsqEWcvYkJVP71YNmTd1OI3i9C92Iqet5LATor5e6ISqwuzgWGwidLvICVNdL4DYRp6VKSIiIqFLAcolVAIUwP6CYq56IpVt2YdJ7tCY5yYPIy5Ge6JEqoy/DDLTgkv9sjOCYxHRzqG9PcZCj9GQ2N67OkVERCSkKEC5hFKAAth18AhXPr6U3XlFjOyexOzrk4mJUmtmkWqRsyUYpnakgvUHx1r2DS71azVA+6ZERETCmAKUS6gFKIAt+w9x1cxUcg6XMK5vK2ZMHEhkhP7lTaRaFebCpnedQLX5fSg5FBxLaO3MSvUYCx3PhuhY7+oUERGRGqcA5RKKAQpg3a48Js5aRkGxj/HJ7Xjw8r4Y/RdwkZrhK4ZvPnX2TWUsgoLdwbHoeOh6PvQY5+yfim/qXZ0iIiJSIxSgXEI1QAGs+CaX6576gqJSPzef3YnfjO2lECVS06yFrDWB86YWwp61wTETAe2GB1ukN+vqXZ0iIiJSbRSgXEI5QAF8lLGPm59No7TMcvdF3bnt/G5elyQS3g7uDLRIXwjbPgV/aXCsaTcnTPUcB22HQISawIiIiNQFClAuoR6gAN5em8Xt81bht/CHi/swaURHr0sSEYCifNjyvjM7tXExFB0MjtVvCt1HO4Gq83lQr4F3dYqIiMhpUYByqQ0BCuCl5Tu4579fAvDQ+P5cNrCtxxWJSDllPti5zAlTX78NB7YFxyLrQeeRzjK/7qOhYSvv6hQREZGTpgDlUlsCFMDsT7byp4UbiIwwPH7NIC7q09LrkkTkeKyF/RnBFumZKwDXn6etBwVbpLfooxbpIiIiIU4ByqU2BSiAf76bwaMfbCYmMoJnbhzCiK7NvC5JRL7PoX3OEr+MhbDlQ/AdCY4ltg+GqQ5nQmS0d3WKiIjIcSlAudS2AGWt5fdvfsXc1O3Uj4nkhSnDGNi+sddliUhllRTCto8Ds1PvwOF9wbF6jaDbKCdQdR0FcYne1SkiIiLfUoByqW0BCsDvt/x8wRpeW72LRnHRzJ+WQo+WCV6XJSIny++H3aucMPX1Qti/ITgWEQUdRgRnpxp39KxMERGRcKcA5VIbAxSAr8zPLS+s4r31e0lKqMcr01Po0DTe67JE5HTkbnVmpTIWwvalYMuCY837BM+baj0QIiK8q1NERCTMhESAMsaMBh4BIoEnrbUPVhhvD8wFEgPX3GOtXWiMuQb4hevSfsAga226MeYjoBVwdIPBRdZa1/qYY9XWAAVQVFrGTc+sYOmWHNo1iWPBtBG0bBTrdVkiUhWOHIBNS5wwtXkJFOcHxxq0CLRIH+t094uO865OERGRMOB5gDLGRAIbgQuBTGAFMNFau951zSxgtbX2cWNMb2ChtbZjhef0Bd6w1nYOvP8IuNtaW+lEVJsDFMChYh/XPPkFa3YepFvzBrzaoRNhAAAgAElEQVQ8LYUm8TFelyUiVclXAts/czr6ZSyCvJ3Bsej60OV8Z3aq2w+gQZJ3dYqIiNRRlQ1Q1bk+ZCiw2Vq71VpbArwEXFLhGgs0DPzcCNh9nOdMBOZVW5W1QIN6Ucy9cQg9WiSwad8hbnh6OQVFpV6XJSJVKSrGCUlj/w4//RKmfwbn/dZZyldaCF+/BW/8BP7RDZ66CD57yGmjHgbLsEVEREJJdc5AXQGMttZOCby/Dhhmrb3NdU0r4F2gMRAPjLLWrqzwnC3AJdbadYH3HwFNgTLgVeCP9nu+RG2fgTpqX34RV8xMZUduIcM6NWHuTUOJjY70uiwRqW55u2DjO87M1LaPoawkONakS3DfVLthEBnlXZ0iIiK1WCgs4bsS+EGFADXUWnu765q7AjX80xiTAjwFnGGt9QfGh+HsnerruqeNtXaXMSYBJ0A9b6199ji/fyowFaB9+/aDt2/fXi3fs6btzC3kiplL2ZtfzAU9mzPzusFER2qjuUjYKC5wzpnKWOSEqiO5wbG4xs4Svx5joOsFUE+dO0VERCorFAJUCvB7a+0PAu9/DWCt/Yvrmq9wZql2Bt5vBYYfbQphjHkI2G+t/fMJfscNQLJ7Vut46soM1FEb9xZw1ROpHCws5ZIBrXnoqgFERBivyxKRmuYvg51fBFuk524JjkXGQKdznDDVfQw0auNdnSIiIrVAKASoKJwmEhcAu3CaSFxtrf3Kdc0i4GVr7TPGmF7A+0Aba601xkQAO4BzrLVbXc9MtNZmG2OicfZGLbHWzvyuWupagAJYs/MgV89exuGSMq4d3p4HLjkDYxSiRMJa9qbA4b2LYMcynG2mAa36B8+batkP9OeFiIhIOZ4HqEARY4GHcVqUz7HW/skYcz+QZq19M9B5bzbQAOdv+l9aa98N3Hsu8KC1drjrefHAJ0B04JlLgLusdR+kcqy6GKAAUrfkMOnp5ZT4/Nx6bhd+Obqn1yWJSKg4nA0bFzuBassHTiOKoxq2DeybGgMdz4Koet7VKSIiEiJCIkCFiroaoACWrN/LtOdXUua33DOmJ9NHdvG6JBEJNaVFsO2T4OzUoT3BsZgEZ79Uj7HQ7UKo38S7OkVERDykAOVSlwMUwBvpu/jpy+lYC3++rC9XD2vvdUkiEqr8fshKD4apveuCYyYS2qdAz8BSvyadvatTRESkhilAudT1AAXw3LLt/O71dRgDj0wYyMX9W3tdkojUBge2B1qkL4RvPgO/LziW1DPYIr1NMkSo46eIiNRdClAu4RCgAP794Wb+vjiDqAjDrOsHc37PFl6XJCK1SVEebF7idPTb9B4U5wXH4pOg+2gnTHU+F2Lqe1WliIhItVCAcgmXAGWt5cF3vuaJj7dSLyqCZ28ayrDOTb0uS0Rqo7JS2L7UWeaX8TYc3BEci4qFzucFWqSPhgT9xxoREan9FKBcwiVAgROifvPaOuYt30GDelHMu3k4fds28rosEanNrIV9G5wglbEIdq10DRpomxxc6pfUUy3SRUSkVlKAcgmnAAVQ5rfc+dJq3lqbReP60SyYnkLX5glelyUidUXBnsC+qUWw9SPwFQXHGncMnjfVPgUio72qUkRE5KQoQLmEW4ACKPH5mfZcGh9m7Kdlw1gWTE+hXRPtWRCRKlZy2AlRXy90QlVhdnAsthF0u8gJVF0vcN6LiIiEKAUol3AMUABHSsqY9PRylm/LpUPT+iyYlkLzhrFelyUidZW/DDLTgi3SszOCYxHRzqG9PcZCj9GQqOMWREQktChAuYRrgAIoKCpl4uxlrNuVT8+WCbw0dTiJ9WO8LktEwkHOlkATioWwIxWsPzjWom9g39QYaD1Q+6ZERMRzClAu4RygAHIOFXPVE6ls2X+Yge0TeX7yMOLrRXldloiEk8Jc2PSuE6Y2vw8lh4JjCa2CTSg6ng3RmikXEZGapwDlEu4BCiAr7whXPJ7KroNHOLNrU56aNITY6EivyxKRcOQrhm8+DcxOLYL8XcGx6Hjoer4Tprr9AOJ1FIOIiNQMBSgXBSjHtuzDXDkzlexDxVzUuwX/uWYQUZERXpclIuHMWshaE1zqt2dtcMxEQLvhwdmpZl29q1NEROo8BSgXBaigDVn5jH8ilfwiH5cPasvfr+hHRIT2HohIiDi4M9gifdsn4C8NjjXtFgxT7YZChGbRRUSk6ihAuShAlbdy+wGuffILjpSWccOIjtz3o94YbeAWkVBTlA9b3nfC1MbFUHQwOFa/KXQf7QSqzudBvQbe1SkiInWCApSLAtSxPt20n8nPpFFS5ufOC7rxswu7e12SiMiJlflg5zInTH39NhzYFhyLrAedRzphqvsYaNjKuzpFRKTWUoByUYA6vnfW7eHWF1bit3DvuF5MObuz1yWJiHw/a2F/RvC8qcwVgOvvstaDAudNjYEWfdQiXUREKkUBykUB6sReWZnJ3QvWAPC3y/tx1ZB2HlckInKSDu1zlvhlLIItH4DvSHCsUfvgeVMdzoQonYMnIiLHpwDlogD13eZ8to3731pPhIF/Xz2IMX21/EVEaqmSQtj2cWB26h04vC84Vq8RdBvlzE51HQVxid7VKSIiIUcBykUB6vs9smQTDy3ZSHSk4clJQxjZPcnrkkRETo/fD7tXBZf67VsfHDOR0KoftB8BHVKgfQrEN/OuVhER8ZwClIsC1Pez1vLAWxuY8/k24qIjeX7KUAZ3aOJ1WSIiVSd3W/C8qe1LwZaVH2/W3QlSHUY4/0xsr/1TIiJhRAHKRQGqcvx+y69eXcuClZkkxEbx8tQUerdu6HVZIiJVr/iQ03xiR6oTpjLTyu+dAmjYJhCoUpyZqqSeEKHDx0VE6ioFKBcFqMrzlfm5fd5qFq3bQ7MGMcyflkLnJJ2vIiJ1nK8EstbAjqWwPdUJVu5zpwDiGkO74c4MVYcR0Ko/REZ7U6+IiFQ5BSgXBaiTU+wrY8rcND7dlE3rRrG8cssIWifGeV2WiEjN8fth/9flA1X+rvLXRNeHtsnBfVRth0BMvDf1iojIaVOAclGAOnmFJT6ue2o5K7cfoHNSPPOnpdCsQT2vyxIR8Ya1cHBHcMnfjlTI3lj+mogoZ1bKvY+qvvaSiojUFgpQLgpQpybvSCkTZi1jQ1Y+vVs1ZN7U4TSK03IVEREADu13gtTRULVnLVh/+WuSelZoTKGz9kREQpUClIsC1KnbX1DMVU+ksi37MEM6NubZm4YRFxPpdVkiIqGnuAB2Lg8EqlTYlQa+ovLXNGpXoTFFD3X6ExEJEQpQLgpQpyfzQCFXzkwlK6+Ikd2TmH19MjFR6kQlIvKdfMWwOz24j2rnMijKK39N/aZOoDoaqlr2h8gob+oVEQlzClAuClCnb/O+Q1z1RCq5h0sY17cVMyYOJDJC/9VURKTS/H7nMF/3PqqCrPLXRMdDuyHBxhRtkiGmvjf1ioiEGQUoFwWoqrFuVx4TZy2joNjH+OR2PHh5X4yWnoiInBpr4cC2QJe/wCxV7pby10REQ+sBwX1U7YapMYWISDVRgHJRgKo6y7flcv2cLygq9XPz2Z34zdheClEiIlWlYG/5xhR71x3bmKJ57/KNKRq18aZWEZE6RgHKRQGqan2YsY+b56bh81vuvqg7t53fzeuSRETqpqL8QGOKwAzVrpVQVlz+msQOwTDVYQQ07arGFCIip0ABykUBquq9tXY3t89bjbXwh4v7MGlER69LEhGp+0qLYPdqV2OKL6A4v/w18UnQfnhwH1WLvmpMISJSCQpQLgpQ1eOl5Tu4579fAvDQ+P5cNrCtxxWJiIQZfxns/Sqw5O9zJ1Qd3lf+mpgG0G6oqzHFYIiO86ZeEZEQpgDlogBVfWZ9soU/L/yayAjD49cM4qI+Lb0uSUQkfFkLuVuDXf62L3UaVbhFxkDrgYElf2c64Sou0Zt6RURCiAKUiwJU9frH4gwe+3AzMZERPHPjEEZ0beZ1SSIiclR+lqsxRarTmAL33/0GWpwRONw3sI8qQf8xTETCjwKUiwJU9bLWct+bX/Fs6nbqx0TywpRhDGzf2OuyRETkeI4cLN+YYvcqKCspf03jTuUbUzTprMYUIlLnKUC5KEBVP7/f8vMFa3ht9S4axUUzf1oKPVomeF2WiIh8n9IjsGtV+cYUJYfKX9OgRYXGFGdARKQ39YqIVBMFKBcFqJpRWubnludXsWTDXpIS6vHK9BQ6NI33uiwRETkZZT7Y+2X5A34Ls8tfU69hoDFFYIaq9SCIjvWmXhGRKqIA5aIAVXOKSsu48ekVpG7NoV2TOBZMG0HLRvpLVUSk1rIWcjaXb0xxcHv5ayLrOd39OqQ4s1TthkJsQ2/qFRE5RQpQLgpQNetQsY9rnvyCNTsP0q15A+ZPS6FxfIzXZYmISFXJ3+0KVKmwbz3lGlOYiEBjCtc+qgbNPStXRKQyFKBcFKBq3oHDJYyflcrGvYfo17YRL0wZRkJstNdliYhIdThyAHZ8Ub4xhd9X/pomXYIzVB1SnEYVakwhIiFEAcpFAcobe/OLuHJmKjtyCxnWqQlzbxpKbLQ2HYuI1HklhbArLbiPaucKKD1c/poGLcsHquZ9ICLCm3pFRFCAKkcByjs7cgq5YuZS9hUUc0HP5sy8bjDRkfoLUkQkrJSVwp61gUAVeBXmlL8mthG0Gx4MVa0HQpSWf4tIzVGAclGA8tbGvQVc9UQqBwtLuWRAax66agAREVq2ISIStqyF7I3l91Hl7Sh/TVQstEkOHvDbbijU0/EYIlJ9FKBcFKC8t2bnQa6evYzDJWVcO7w9D1xyBkZr30VE5Ki8zPKt0/dvKD9uIqFl32BjivYp0CDJm1pFpE5SgHJRgAoNS7dkc8PTKyjx+bn13C78cnRPr0sSEZFQVZgLO5bB9s+dWaqsNcc2pmjarfw+qsQOakwhIqcsJAKUMWY08AgQCTxprX2wwnh7YC6QGLjmHmvtQmNMR2ADkBG4dJm1dnrgnsHAM0AcsBC4037Pl1CACh1L1u9l2vMrKfNb7hnTk+kju3hdkoiI1AYlhyFzRXCWKjMNSgvLX5PQ2glSHUY4oSqppxpTiEileR6gjDGRwEbgQiATWAFMtNaud10zC1htrX3cGNMbWGit7RgIUG9Za884znOXA3cCy3AC1Axr7aLvqkUBKrS8vnoXP5ufjrXw58v6cvWw9l6XJCIitU1ZqTMrdXQf1Y5Up526W2xi4ByqwCxVq/5qTCEiJ1TZABVVjTUMBTZba7cGCnoJuARY77rGAkePKm8E7P6uBxpjWgENrbWpgffPApcC3xmgJLRcOrANBUWl/O6Nr/jt61/SIDaKi/u39rosERGpTSKjoW2y8zrzDvD7ITujfGOK/EzYuMh5AUTFOdcf3UfVdgjUa+Dt9xCRWqc6A1QbYKfrfSYwrMI1vwfeNcbcDsQDo1xjnYwxq4F84F5r7aeBZ2ZWeGabKq5basB1KR3JL/Lx98UZ3PVyOgn1ojivp06pFxGRUxQRAc17Oa8hk53PDu5wAtXRUJW9Eb751HmB05iiVf/yjSnim3r3HUSkVqjOAHW8XZwV1wtOBJ6x1v7TGJMCPGeMOQPIAtpba3MCe55eN8b0qeQznV9uzFRgKkD79loiFopuPbcL+UdKeeKTrUx/fiXP3jSUYZ31F5eIiFSRxPbOq/8E5/3h7ODs1I6lkLUWdq9yXqmPOdc06+EEqqOhKrGdd/WLSEiqzgCVCbj/1GnLsUv0JgOjAay1qcaYWKCZtXYfUBz4fKUxZgvQPfDMtt/zTAL3zQJmgbMH6rS/jVQ5Ywz3jOlJflEp85bvZPLcNObdPJy+bRt5XZqIiNRF8c2g14+cF0DxIchcHjzgN3OFswwwOwNWPu1c06hd+X1UST3U6U8kzFVngFoBdDPGdAJ2AROAqytcswO4AHjGGNMLiAX2G2OSgFxrbZkxpjPQDdhqrc01xhQYY4YDXwDXA49W43eQamaM4Y+X9qWgyMdba7OY9PRy5k8bTtfmOixRRESqWb0G0OV85wXgK4Gs9PKNKfJ2wpc74cv5zjVxTSo0pujn7McSkbBR3W3MxwIP47Qon2Ot/ZMx5n4gzVr7ZqDz3mygAc5SvF9aa981xlwO3A/4gDLgPmvt/wLPTCbYxnwRcLvamNd+JT4/U59L46OM/bRsGMuC6Sm0a1Lf67JERCSc+f2wb31g2V8gVBVklb8mOv7YxhQx+vtLpDbyvI15KFGAqh2OlJQxac5yln+TS4em9VkwPYXmCbFelyUiIuKwFg58Uz5Q5Wwuf01EFLQaEJyhaj8c6jfxpFwROTkKUC4KULVHflEpV89exrpd+fRsmcDLU1NoVF9LI0REJEQd2le+McWeL8H6y1/TvHdg2V9glqqRGgiLhCIFKBcFqNol51AxVz2Rypb9hxnYPpHnJw8jvl51btcTERGpIkX5FRpTpEFZcflrEts7s1NHZ6madVNjCpEQoADlogBV+2TlHeGKx1PZdfAIZ3ZtypwbhlAvKtLrskRERE6Orxh2rw6eR7XzCyjOL39N/WbOUr+jM1Qt+0Gk/sOhSE1TgHJRgKqdtmUf5sqZqWQfKuYHfVrw76sHERUZ4XVZIiIip85fBnu/Kr+P6tDe8tfENHCaURw9j6rNYIiO86ZekTCiAOWiAFV7bcjKZ/wTqeQX+bh8UFv+fkU/IiK0zEFEROoIayF3a/l9VLlby18TEQ1tBgX3UbUbBnGJ3tQrUocpQLkoQNVuK7cf4Nonv+BIaRk3jOjIfT/qjdFacRERqasK9lRoTLEO57SXowy06AOtB0LTrtC0i/PPxp0gWt1rRU6VApSLAlTt9+mm/Ux+Jo2SMj93XtCNn13Y3euSREREakZRHuxcHlzyt2sllJUc50IDjdpB085OoGrSJRiwEtvrwF+R76EA5aIAVTe8sy6LW19Yhd/CveN6MeXszl6XJCIiUvNKi2D3KmcvVc4WyN3inEd1YDvYsuPfExEFjTu6QpUrZDVsAxHaYyyiAOWiAFV3LEjbyS9eWQvA3y7vx1VD2nlckYiISIgoK3VC1NFAlbPZCVg5WyA/88T3RcVCk87BpYDumav4JLVYl7BR2QClHplSq1yZ3I6CIh/3v7Wee/67loTYKMb0beV1WSIiIt6LjIZmXZ0XPyg/VlIIB7YFAtXmQMgKvA7vg33rnVdF9RoGwlXXCgGrixpZSNhSgJJa56azOpFfVMrDSzZxx0ureapeFOd0T/K6LBERkdAVU99pPNGiz7FjRXmBpYBbXbNWgX8W50FWuvOqqH7TQLDqWj5kNekMMfHV/51EPKIlfFIrWWt54K0NzPl8G3HRkTw/ZSiDOzTxuiwREZG6w1oozKkwa+VaFug7cuJ7E1oHZqwqLAts3BGiYmrsK4icDO2BclGAqpv8fssvX13LKyszSYiN4uWpKfRu3dDrskREROo+vx8Kso4NVblbIHcb+EuPf5+JcDoClttrFZi9atQOIiJr9nuIuChAuShA1V2+Mj+3vbiad77aQ7MGMcyflkLnpAZelyUiIhK+ynyQt7N8h8CjIevgDsqfaeUSGeOcZXV05urbgNUVElqqmYVUOwUoFwWouq3YV8aUuWl8uimbNolxLJieQuvEOK/LEhERkYp8xXDgm/J7rY7uvSrIOvF90fHOTJW7Q+DRWaz6TRSupEooQLkoQNV9hSU+rn3yC1btOEjnpHjmT0uhWYN6XpclIiIilVV8KBim3F0CczbDkdwT3xebeGyHwKMhq15CzdUvtZ4ClIsCVHjIKyxl/KxUvt5TQJ/WDZk3dTgNY3XquoiISK1XmBsIV8dpaFFy6MT3NWjhClWukNWkE0RrtYqUpwDlogAVPvYXFHPlzKV8k1PIkI6NefamYcTFaEOqiIhInWQtHNp3/C6BuVuhrPgENxqnacXRBhbupYGJ7Z0ztSTsKEC5KECFl8wDhVw5M5WsvCJGdk9i9vXJxERFeF2WiIiI1CS/H/Izy59rdTRkHdgOtuz490VEQWIH114rV0OLhm0gQv9OUVcpQLkoQIWfzfsOcdUTqeQeLmFc31bMmDiQyAhtMBURERGgrNQJUeVmrQL/zM888X1RsYFDg7sc29AiPknNLGo5BSgXBajwtG5XHhNnLaOg2MeEIe34y4/7YvQHm4iIiHyXkkI4sK3CfqvA6/C+E98Xk1B+r9W3SwM7Q1zjmqtfTpkClIsCVPhavi2X6576gmKfn5vP7sRvxvZSiBIREZFTU5R/bIfAo7NYRXknvq9+0wpdAo82tOgMMfE1V798JwUoFwWo8PZhxj5unpuGz2+5+6Lu3HZ+N69LEhERkbrEWijMOU6XwK3Oz6WFJ743ofWxXQKbdoXGHSEqpsa+glRxgDLGdAEyrbXFxphzgX7As9bag6ddaQ1QgJL/rdnNHS+txlq4/5I+XJ/S0euSREREJBxY6xwSnLO5QpfALZC7Dfylx7/PRDgdAcvttQrsvUpsDxHqMlzVqjpApQPJQEdgMfAm0MNaO/Y066wRClACMG/5Dn793y8BeGh8fy4b2NbjikRERCSslfkgb2f5DoFHZ7EO7gBO8O/pkTHODNXRYOUOWQmt1MziFFU2QEVV8nl+a63PGHMZ8LC19lFjzOrTK1GkZk0c2p6ColL+vPBr7l6wlviYKC7q09LrskRERCRcRUY5h/o26QSMKj/mK4YD35QPVblbnX8WZEH2RudVUXR8sFNgxYYW9ZsoXFWBygaoUmPMRGAS8KPAZzphTGqdqed0Ie9IKf/+cAu3zVvNMzcMYUTXZl6XJSIiIlJeVD1I6uG8Kio+5ISpiocH52yGI7mw90vnVVFsYoW9Vq5lgbENq/871RGVXcLXG5gOpFpr5xljOgHjrbUPVneBVUFL+MTNWsv/vfEVzy3bTv2YSF6YMoyB7dVeVEREROqAwtzATNVxGlqUFJz4vvjmgdmqzuWbWTTpBNFxNVe/h6qtC58xpjHQzlq79lSLq2kKUFKR32/5+YI1vLZ6F43iopk/LYUeLRO8LktERESkelgLh/a5ApW7ocVWKCs+wY0GGrU9/uHBie0hsu4sSqvqJhIfARfjLPlLB/YDH1tr7zrNOmuEApQcT2mZn1ueX8mSDftonlCPBdNT6NBUZzGIiIhImPH7IT/z2L1WOVucfVi27Pj3RURBYodjuwQ27QoN20BERI1+jdNV1QFqtbV2oDFmCs7s033GmLXW2n5VUWx1U4CSEykqLePGp1eQujWHdk3iWDBtBC0bxXpdloiIiEhoKCuFA9uP7RKYu9XpIHgiUbFOM4smncvPWjXpAg2ah2Qzi6oOUF8CFwFzgd9aa1coQEldcajYxzWzl7EmM49uzRswf1oKjeN1cJ2IiIjIdyo94pxlVW6vVWBZ4OF9J74vJiG416rXxdDn0pqr+TtUdRvz+3HOf/o8EJ46A5tOp0CRUNGgXhTP3DiUq55IZdO+Q0x6ejkvTBlGQmzdWdMrIiIiUuWi46BFb+dVUVH+/7d35/FRnve9978/jfYdaSQkFrEK24C8AAZv4DiOYxvnxE2TJnZqx46dJunJ0pOn6alz2qSpz+lz0tfp06R5kjR14pjYTuImcZO6MdRZ7Bi8YcDGCDBGC5tAQhoJoX2/zh8zSPcIAQPScGukz/v1mhcz93XP8JvbYzRf3df9uyKhqub0hhY9J6X6t8K3wsWTJkDF6rybSCQizkAhFsfbevSh776iIy3dWrOgQD98YLXSU1jlGwAAYMI4F+4UeCpQzVwulU6OSW2xnoGK6couM5tjZr8ws0YzO25mT5vZnPGXCUweM3PT9aMHr1FxTpq2HmjRZ370hvoHh/wuCwAAYOowk7IKpbI10pUfnTTh6XzE2hrjMUnPSJolabak/4hsA6aUssJMPfHgGuVnpuh3+xr1xZ+9paGhqX+WFgAAALGJNUAVOecec84NRG4bJBXFsS7AN5eU5GjDx1crKzWgf995TF95Zremw1RXAAAAnFusASpkZveYWSByu0dSczwLA/x05dx8fe++VUpNTtKTrx3W/3nuHb9LAgAAwCQQa4B6QNKHJTVIqpf0IUkfj1dRwGRw3aKgvv3RFQokmb7z+xp998Uav0sCAACAz2IKUM65w8659zvnipxzxc65P5D0h3GuDfDdLUtn6v/7oytkJn1t0z79eOthv0sCAACAj2I9AzWW/2fCqgAmsT+4arYefv8ySdJf/bJS//HWMZ8rAgAAgF/GE6BswqoAJrl7r52vv7j1EjknfeFfd+qFfWdZXRsAAABT1ngCFG3JMK3813ct0qfWLdTAkNOnn9yhrbX0UQEAAJhuzhqgzKzdzNrGuLUrvCYUMG2YmR66/VLdvXquegeG9OAPt6uy7qTfZQEAAOAiOmuAcs7lOOdyx7jlOOeSL1aRwGRhZvpff1ChOy4vVUfvgO577HVVN7b7XRYAAAAukvFM4QOmpUCS6esfvlLvuqRILZ19uuf7r+tIS5ffZQEAAOAiiGuAMrPbzOwdM6s2s4fGGC8zsxfM7E0z22Vm6yPbbzGzHWZWGfnz3Z7n/D7ymjsjt+J4vgdgLKnJSfrnP16pq+fPUENbj+59dKsa23v8LgsAAABxFrcAZWYBSd+WdLukpZLuNrOlo3b7a0k/dc5dJekuSd+JbA9J+i/OuQpJ90l6YtTz/tg5d2XkRjs0+CIjNaBH779ay2bl6mBzlz726Os62dXvd1kAAACIo3iegVotqdo5V+uc65P0lKQ7R+3jJOVG7udJOiZJzrk3nXOnFtvZIyndzNLiWCtwQXLTU/T4A6u1sChL+xradf+G19XZO+B3WQAAAIiTeAao2ZKOeB7XRbZ5fVXSPWZWJ2mjpM+N8ToflPSmc67Xs+2xyPS9L5vZmOtRmdknzWy7mW1vamq64DcBnEthdpqefHCNZudn6M3DrfrUEzvUOzDodzElsREAACAASURBVFkAAACIg3gGqLGCzei1o+6WtME5N0fSeklPmNlwTWa2TNLfS/qU5zl/HJnatzZyu3esv9w594hzbpVzblVRUdE43gZwbrPyM/TkJ9YomJ2ml6pD+vxP3tTA4JDfZQEAAGCCxTNA1Uma63k8R5Epeh4PSvqpJDnnXpWULikoSWY2R9IvJH3MOVdz6gnOuaORP9sl/VjhqYKA7xYEs/T4A6uVm56s5/Yc118+XamhIdabBgAAmEriGaC2SSo3swVmlqpwk4hnRu1zWNLNkmRmlykcoJrMLF/Ss5K+5Jx7+dTOZpZsZqcCVoqk90naHcf3AJyXpbNy9djHr1ZGSkBPv1Gnh3+1V84RogAAAKaKuAUo59yApM9Kek7S2wp329tjZg+b2fsju/25pD8xs7ck/UTS/S78bfOzkhZL+vKoduVpkp4zs12Sdko6Kul78XoPwIVYOa9Aj3xspVIDSdrwykF947dVfpcEAACACWLT4bfjq1atctu3b/e7DEwz/7m7Xv/1R29oyElfft9SPXjDAr9LAgAAwBmY2Q7n3Kpz7RfXhXSB6ey25aX62gcvlyT9z1/t1U+3HznHMwAAADDZEaCAOPrwqrn6yvvC60c/9PQubaqs97kiAAAAjAcBCoizB25YoD+7uVxDTvr8U29q837WJQMAAEhUBCjgIvhv7ynXx6+fr/5Bp089sUM7DrX4XRIAAAAuAAEKuAjMTF++Y6k+tHKOuvsHdf9j27T3WJvfZQEAAOA8EaCAiyQpyfS1P6zQrctmqr1nQB/7wVbVNnX4XRYAAADOAwEKuIiSA0n65t1XaW15UKGOPt376Os61trtd1kAAACIEQEKuMjSkgP67j0rdVVZvo62duueR7cq1NHrd1kAAACIAQEK8EFWWrI23L9al5bkqLapU/f94HW19fT7XRYAAADOgQAF+CQvM0WPP7ha8wsztedYmx7csE3dfYN+lwUAAICzIEABPirOSdeTn1ijktx0bTt4Qp9+cof6Bob8LgsAAABnQIACfDZnRqae/MRqFWSl6sX9TfrCT3dqcMj5XRYAAADGQIACJoHFxTl6/IHVyklL1rO76vVXv6iUc4QoAACAyYYABUwSy2fn6fv3rVJacpKe2nZE/3vTPkIUAADAJEOAAiaRNQsL9d17Vio5yfTI5lp95/c1fpcEAAAADwIUMMncdGmxvv6RK2Um/Z/n3tHjrx70uyQAAABEEKCASei/XDFL/+8HKiRJX/n3PfrFm3U+VwQAAACJAAVMWnevLtOXbr9UkvTFn+3Sb/Ye97kiAAAAEKCASexTNy7SZ25apMEhp8/8+A29Uh3yuyQAAIBpjQAFTHJffO8luveaeeobGNInHt+unUda/S4JAABg2iJAAZOcmelv379Md145S119g7r/sdf1TkO732UBAABMSwQoIAEkJZn+4Y+u0HsuK1ZrV7/ufXSrDjV3+l0WAADAtEOAAhJESiBJ3/roCl2zsECN7b2659GtOt7W43dZAAAA0woBCkgg6SkBff++q3XFnDwdaenWPd/fqhOdfX6XBQAAMG0QoIAEk52WrA0fX63y4mxVNXbovsdeV3tPv99lAQAATAsEKCABzchK1RMPrtHcggztqjupP3l8u3r6B/0uCwAAYMojQAEJqiQvXT968BoV56TptdoWffbHb6h/cMjvsgAAAKY0AhSQwMoKM/XEg2uUl5Gi377dqC/+7C0NDTm/ywIAAJiyCFBAgrukJEc/fGC1slID+vedx/SVZ3bLOUIUAABAPBCggCngyrn5+t59q5SanKQnXzusf/j1O36XBAAAMCURoIAp4rpFQX37oysUSDJ9+4UafffFGr9LAgAAmHIIUMAUcsvSmfqHP7pckvS1Tfv0462Hfa4IAABgaiFAAVPMB66ao4fvXCZJ+qtfVuo/3jrmc0UAAABTBwEKmII+du18ffG9S+Sc9IV/3akX9jX6XRIAAMCUQIACpqjP3LRYn1y3UANDTp9+coe21jb7XRIAAEDCI0ABU5SZ6Uu3X6q7rp6r3oEhPfjD7aqsO+l3WQAAAAmNAAVMYWamv/tAhe6oKFVH74Due+x1VTd2+F0WAABAwiJAAVNcIMn09Y9cqRuXFKmls0/3fH+rjrR0+V0WAABAQiJAAdNAanKSvnvPSl09f4Ya2np076Nb1dje43dZAAAACYcABUwTGakBPXr/1Vo2K1cHm7v0sUdf18mufr/LAgAASCgEKGAayU1P0Q8fWK2FRVna19Cuj294XZ29A36XBQAAkDAIUMA0E8xO05MPrtHs/Ay9cbhVn3pih3oHBv0uCwAAICEQoIBpaFZ+hp54cLWC2al6qTqkz//kTQ0MDvldFgAAwKRHgAKmqYVF2Xr8gTXKSU/Wc3uO66F/q9TQkPO7LAAAgEmNAAVMY0tn5WrDx69WRkpAP99Rp4d/tVfOEaIAAADOJK4BysxuM7N3zKzazB4aY7zMzF4wszfNbJeZrfeMfSnyvHfM7NZYXxPA+Vk5r0D/cu9KpQRMG145qG/8tsrvkgAAACatuAUoMwtI+rak2yUtlXS3mS0dtdtfS/qpc+4qSXdJ+k7kuUsjj5dJuk3Sd8wsEONrAjhP65YU6Zt3XaUkk/7pd1V69KUDfpcEAAAwKcXzDNRqSdXOuVrnXJ+kpyTdOWofJyk3cj9P0rHI/TslPeWc63XOHZBUHXm9WF4TwAW4vaJUX/vg5ZKk//mrvfrp9iM+VwQAADD5xDNAzZbk/QZWF9nm9VVJ95hZnaSNkj53jufG8poALtCHV83Vl98XPqn70NO7tKmy3ueKAAAAJpd4BigbY9voq9PvlrTBOTdH0npJT5hZ0lmeG8trhv9ys0+a2XYz297U1HQeZQPT24M3LNDnby7XkJM+/9Sb2ryf/38AAABOiWeAqpM01/N4jkam6J3yoKSfSpJz7lVJ6ZKCZ3luLK+pyOs94pxb5ZxbVVRUNI63AUw/X3hPue6/br76B50+9cQO7TjU4ndJAAAAk0I8A9Q2SeVmtsDMUhVuCvHMqH0OS7pZkszsMoUDVFNkv7vMLM3MFkgql/R6jK8JYJzMTF9531J9cMUcdfcP6v7HtmnvsTa/ywIAAPBd3AKUc25A0mclPSfpbYW77e0xs4fN7P2R3f5c0p+Y2VuSfiLpfhe2R+EzU3sl/aekzzjnBs/0mvF6D8B0lpRk+vsPVujWZTPV3jOgj/1gqw6EOv0uCwAAwFc2HRbNXLVqldu+fbvfZQAJqXdgUA9u2K6XqkOanZ+hn336Ws3Kz/C7LAAAgAllZjucc6vOtV9cF9IFkPjSkgP6l3tX6qqyfB1t7dY9j25VqKPX77IAAAB8QYACcE5ZacnacP9qXVqSo9qmTt3yjy/qoad3afP+JvUPDvldHgAAwEXDFD4AMWts79Enfrhdu+pODm/Lz0zRrUtLtP7yUl23qFApAX4vAwAAEk+sU/gIUADOi3NO7xxv18Zd9Xq2sl41TSONJfIyUvTepTO1/vJSXb8oqNRkwhQAAEgMBCgPAhQQH8457T/eoY2V9dpYWa+qxo7hsdz0ZN2ytER3XF6iGxYXEaYAAMCkRoDyIEABF0fV8XY9GwlT+4+PhKmc9GTdsnSm7qgo1Q3lQaUlB3ysEgAA4HQEKA8CFHDxVTe2a2NlgzZW1mtfQ/vw9py0ZL1n6UytryjV2vKg0lMIUwAAwH8EKA8CFOCvmqYObaqs17OVDXq7vm14e3Zast5zWbFuryjVjUuKCFMAAMA3BCgPAhQwedQ2dWjT7gY9u6teez1hKis1oJsvC5+ZetclhCkAAHBxEaA8CFDA5HQw1KmNu8PXTO0+OhKmMlMDevelxbqjolTvuqRYGamEKQAAEF8EKA8CFDD5HWru1Kbd4WumvOtMZaQE9O7LirV+ealuurRImanJPlYJAACmKgKUBwEKSCxHWrqGW6O/NSpM3XRpkdZXlOqmS4qVlUaYAgAAE4MA5UGAAhJX3Ykubaps0LOV9dp5pHV4e3pKkt61pFjrLy/VzZcSpgAAwPgQoDwIUMDUcLS1W5siZ6beODwSptKSk/SuS8Jnpm6+bKayCVMAAOA8EaA8CFDA1HOstXv4mqkdh04Mb09NTtKNS4p0R0Wpbr6sWDnpKT5WCQAAEgUByoMABUxtDSd7tCnSzW/7oRM69c9aanKS1pUXaX1Fid6zdKZyCVMAAOAMCFAeBChg+jje1hOe5re7QdsOtoyEqUCS1pYHtb6iVO9ZOlN5GYQpAAAwggDlQYACpqfGth79557wor2ve8JUSsB0w+JwmHrv0hLlZRKmAACY7ghQHgQoAI3tPXpuz3Ft3FWvrQeaNeQJU9cvDmr98lK9d9lM5Wem+lsoAADwBQHKgwAFwKupvVfP7WnQpt31erVmJEwlJ5muWxzUHRUleu/SEs3IIkwBADBdEKA8CFAAzqS5ozd8ZqqyXq/WNmswkqYCSabrFhVqfUWpbl1WogLCFAAAUxoByoMABSAWLZ19+vWe8KK9r9REh6lrFxbq9ooS3basRIXZaT5XCgAAJhoByoMABeB8nejs06/3NmhjZYNerg5pIBKmkky6ZmH4zNRty0sUJEwBADAlEKA8CFAAxqO1q0+/3hue5vdydUj9gyNhavWCAt1RUapbl5eoOCfd50oBAMCFIkB5EKAATJSTXf36zdvhMLWlqmk4TJlJq+cXaH1FqW5fXqLiXMIUAACJhADlQYACEA8nu/v1273HtWl3vTbvD6lvcEhSOExdPa9A6ytKdHtFqWYSpgAAmPQIUB4EKADx1tbTr9+9fVzP7mrQ5qom9Q2MhKmVZTPCZ6YqSlSal+FzpQAAYCwEKA8CFICLqb2nX8/va9Szu+r1+/0jYUqSVs6bMTzNb1Y+YQoAgMmCAOVBgALgl47eAf3u7ePaVNmgF95pVK8nTF1Vlq87Kkp1e0WpZhOmAADwFQHKgwAFYDLo7B3Q8/satbGyXi+806ie/pEwdcXcfN1RUaLbl5dqbkGmj1UCADA9EaA8CFAAJpuuvgG9sK9JGyvr9fy+RnX3Dw6PXTEnT+srSrW+gjAFAMDFQoDyIEABmMy6+gb0+3dGwlRX30iYqpgdDlN3VJSqrJAwBQBAvBCgPAhQABJFd9+gXtzfqGcrG/S7t49Hhanls3PDZ6aWl2p+MMvHKgEAmHoIUB4EKACJqKd/UC/uD5+Z+u3e4+r0hKmlpbm64/LwNL8FhCkAAMaNAOVBgAKQ6Hr6B7V5f5M27W7Qb/ceV3vvwPDYpSU5uqOiVOsvL9WiomwfqwQAIHERoDwIUACmkt6BQW3ZH9LGynr9ZowwFW5AUaLFxTk+VgkAQGIhQHkQoABMVb0Dg3q5OqRndzXo13sb1N4zEqaWzMwebkBRPpMwBQDA2RCgPAhQAKaDvoGhcJiqrNev9zSozROmFhePhKklM7NlZj5WCgDA5EOA8iBAAZhu+gaG9EpNeJrfr/ceV2tX//DYoqIs3VFRqtsrSnVpSQ5hCgAAEaCiEKAATGf9g0N6taZZGyvr9dyeBp3whKmFwazhRXsvKyVMAQCmLwKUBwEKAML6B4f0Wu2pMHVcLZ19w2MLglm6fXmJ1leUatmsXMIUAGBaIUB5EKAA4HQDg0PaeqBFz1bW67ndDWr2hKl5hZnDi/Yun02YAgBMfQQoDwIUAJzdwOCQXj8VpvY0KNQxEqbKCjJ1e0WJ7qgoVcXsPMIUAGBKIkB5EKAAIHaDQ06vH2jRxsp6bdrdoFBH7/DYnBkZw9dMXTGHMAUAmDoIUB4EKAC4MINDTtsPjoSpxvaRMDU7P0PrK0p0e0WprpqbT5gCACQ0ApQHAQoAxm9oyGn7oRORMFWv420jYWpWXrpuj5yZumpuvpKSCFMAgMQyKQKUmd0m6Z8kBSR93zn3tVHjX5d0U+RhpqRi51y+md0k6eueXS+VdJdz7pdmtkHSjZJORsbud87tPFsdBCgAmFhDQ05vHD6hZyvrtamyQQ1tPcNjpXnpum15+JqpFWUzCFMAgITge4Ays4Ck/ZJukVQnaZuku51ze8+w/+ckXeWce2DU9gJJ1ZLmOOe6IgHqV865n8daCwEKAOJnaMjpzSMntLGyQZsq63Xs5EiYKsmNhKnLS7WSMAUAmMRiDVDJcaxhtaRq51xtpKCnJN0pacwAJeluSX8zxvYPSdrknOuKS5UAgHFJSjKtnFeglfMK9FfrL9POulZt3BW+Zupoa7c2vHJQG145qOKctOF1plbNL1CAMAUASEDxPAP1IUm3Oec+EXl8r6Q1zrnPjrHvPEmvKXyWaXDU2POS/tE596vI4w2SrpXUK+l3kh5yzvVqFDP7pKRPSlJZWdnKQ4cOTeC7AwCci3NOb9Wd1MbKej27q15HW7uHx4o8YepqwhQAYBKYDFP4/kjSraMC1Grn3OfG2PcvFQ5Pnxu1vVTSLkmznHP9nm0NklIlPSKpxjn38NlqYQofAPjLOadddSe1cXe9NlbW60jLSJgKZqfptuUztb6iVGsWFBKmAAC+mAxT+OokzfU8niPp2Bn2vUvSZ8bY/mFJvzgVniTJOVcfudtrZo9J+uIE1AoAiCMz0xVz83XF3Hw9dNul2n20Tc9WhsPU4ZYuPfnaYT352mEFs1P13mXhBhRrFhQoOZDkd+kAAESJ5xmoZIWbSNws6ajCTSQ+6pzbM2q/SyQ9J2mBG1WMmb0m6UvOuRc820qdc/UWXnDk65J6nHMPna0WzkABwOTknNOeY23aGAlTB5tHLnctzBoJU9csJEwBAOLL9yl8kSLWS/qGwm3Mf+Cc+zsze1jSdufcM5F9viopfXQIMrP5kl6WNNc5N+TZ/rykIkkmaaekTzvnOs5WBwEKACY/55z21rdpU2WDNlbWqzbUOTw2IzNFty4LXzN17aJCpRCmAAATbFIEqMmCAAUAicU5p30N7eEGFJX1qm0aCVP5mSl679LwNVPXLw4SpgAAE4IA5UGAAoDE5ZzT/uMdw9dMVTeOTDrIy4iEqctLdf2ioFKTCVMAgAtDgPIgQAHA1LH/ePvwNVP7j4+Eqdz0ZN2ytER3XF6iGxYXEaYAAOeFAOVBgAKAqam6sV3P7gpfM/XO8fbh7TnpybrlsvA0v7VLgkpLDvhYJQAgERCgPAhQADD1VTd2aFPkmql9DZ4wlZas90SumVpbHlR6CmEKAHA6ApQHAQoAppfapg5t2t2gZ3fVa2992/D27LRk3XxZsdZXlOqGxUFlpcVzOUQAQCIhQHkQoABg+joQ6hy+ZmrPsZEwlRIwrSiboXVLirSuvEjLZuUqKcl8rBQA4CcClAcBCgAgSYeaO7WxskG/3tugt460asjzI7AgK1U3LA5q3ZIirS0PamZuun+FAgAuOgKUBwEKADDaya5+vVIT0uaqJm3eH9LR1u6o8UtLcrS2PByorp5fwLVTADDFEaA8CFAAgLNxzqk21Kkt+5u0uSqkV2ua1d0/ODyelpykNQsLtS4SqMqLs2XGdD8AmEoIUB4EKADA+egdGNSOQye0pSqkzfuboq6dkqSS3HStLQ9q7ZIi3bA4qIKsVJ8qBQBMFAKUBwEKADAeoY5evRQJU5urQgp19A6PmUkVs/O0rjx87dSKeTOUEmARXwBINAQoDwIUAGCiOOf0dn27tlQ1aUtVSK8faFHf4NDweFZqQNcuCurGJUGtLS/S/GCWj9UCAGJFgPIgQAEA4qW7b1BbDzRr8/6QtlQ1qaqxI2q8rCBzuBnFtYsKlZue4lOlAICzIUB5EKAAABfLsdZuvVQV0otVTXqpKqST3f3DY4Ek04qyfK0tL9K6JUWqmJ2nAGtPAcCkQIDyIEABAPwwOORUefRkpLtfk9443KpBz+JT+Zkpun5xUOvKw9P9ZuVn+FgtAExvBCgPAhQAYDJo6+nXqzXNkWYUTTrSEr321OLi7OHpfmsWFCgzNdmnSgFg+iFAeRCgAACT0cFQp7ZUjaw91dE7MDyWGkjS1QtmhKf7lRfpstIc1p4CgDgiQHkQoAAAk13/4JDePNyqzfubtKWqSbuOnpT3R3QwOy081W9JUDcsLlJRTpp/xQLAFESA8iBAAQASTUtnn16uDg1P9zve1hs1vrQ0V+uWFGldeVAr589QWnLAp0oBYGogQHkQoAAAicw5p6rGjuGFfLfWNqt3YGTtqYyUgK5ZWKB1S4q0trxIi4qymO4HAOeJAOVBgAIATCU9/YPadrBFW6rCZ6j2NbRHjc/OzxhuRnH9oqDyMll7CgDOhQDlQYACAExlx9t6tKUqvJDvlqqQWjr7hseSTLpibnjtqRuXBHXFnHwlB5J8rBYAJicClAcBCgAwXQwNOe2tb9OLkWYUOw6dUP/gyM/6nPRkXb8o3IxiXXmR5hZk+lgtAEweBCgPAhQAYLrq6B3Q1trmSHe/kGpDnVHjC4JZwwv5XruoUFlprD0FYHoiQHkQoAAACDvS0jV87dTLNSG194ysPZUSMK0omxHp7lekZbNylZREMwoA0wMByoMABQDA6QYGh/RWXas27w9pc1WT3jrSqiHP14KCrFTdsDgY6e4X1MzcdP+KBYA4I0B5EKAAADi3k139erkm3Ixi8/6QjrZ2R41fWpIz3N3v6vkFSk9h7SkAUwcByoMABQDA+XHOqTbUOXzt1Ks1zeruHxweT0tO0pqFhVoXCVTlxdmsPQUgoRGgPAhQAACMT+/AoHYcOqHN+8NnqPYca4saL8lN19ryoNYuKdINi4MqyEr1qVIAuDAEKA8CFAAAE6upvVcvV4ebUWyuCinU0Ts8ZiZVzM7TuvLwtVMr5s1QCmtPAZjkCFAeBCgAAOLHOae369vD105VNWnbgRPqGxwaHs9KDejaRUHduCTcLn1+MMvHagFgbAQoDwIUAAAXT3ffoF470Kwtke5+1Y0dUeNlBZnDzSiuXVSo3PQUnyoFgBEEKA8CFAAA/jnW2h05OxXSS1UhnezuHx4LJJlWlOVrbXmR1i0pUsXsPAVYewqADwhQHgQoAAAmh8Ehp8qjJyPd/Zr0xuFWDXoWn8rPTNH1i4NaVx6e7jcrP8PHagFMJwQoDwIUAACTU1tPv16taY40o2jSkZbotacWF2cPT/dbs6BAmanJPlUKYKojQHkQoAAASAwHQ53aUtWkF/eH9GpNSJ19I2tPpQaSdPWCGeHpfuVFuqw0h7WnAEwYApQHAQoAgMTTPzikNw6d0JaqcDOKyqMn5f3aEsxOC0/1WxLUDYuLVJST5l+xABIeAcqDAAUAQOJr6ezTS9UhbYlM9zve1hs1vrQ0V+uWFGldeVAr589QWnLAp0oBJCIClAcBCgCAqcU5p6rGjuGFfLfWNqt3YGTtqYyUgK5ZWKB1S4q0trxIi4qymO4H4KwIUB4EKAAAprae/kFtO9gS6e4X0r6G9qjx2fkZw80orl8UVF4ma08BiEaA8iBAAQAwvRxv6wlfO7W/SS9Vh9TS2Tc8lmTSFXPDa0/duCSoK+bkKzmQ5GO1ACYDApQHAQoAgOlraMhpz7E2ba5q0ub9Tdpx6IQGPGtP5aQn6/pF4WYU68qLNLcg08dqAfiFAOVBgAIAAKd09A7otZpmbakKXz91INQZNb4gmDW8kO+1iwqVlcbaU8B0QIDyIEABAIAzOdLSpc1VTdqyP6SXa0Jq7xkYHksJmFaUzYh09yvSslm5SkqiGQUwFRGgPAhQAAAgFgODQ3qrrlUv7g9pS1WT3jrSKs9sPxVkpeqGxcFId7+gZuam+1csgAk1KQKUmd0m6Z8kBSR93zn3tVHjX5d0U+RhpqRi51x+ZGxQUmVk7LBz7v2R7QskPSWpQNIbku51zvXpLAhQAADgQrR29emVmuZwu/T9TTp2sidq/NKSnOHuflfPL1B6CmtPAYnK9wBlZgFJ+yXdIqlO0jZJdzvn9p5h/89Juso590DkcYdzLnuM/X4q6d+cc0+Z2XclveWc++ez1UKAAgAA4+WcU01TZ/jaqf1Neq22Rd39g8PjaclJWrOwUOsigaq8OJu1p4AEMhkC1LWSvuqcuzXy+EuS5Jz732fY/xVJf+Oc+03k8WkBysL/CjVJKnHODYz+O86EAAUAACZa78Cgdhw8oc2Rdul769uixkty07W2PKi1S4p0w+KgCrJSfaoUQCxiDVDxbCszW9IRz+M6SWvG2tHM5klaIOl5z+Z0M9suaUDS15xzv5RUKKnVOXfq6s66yN8DAABwUaUlB3Td4qCuWxzUQ7dfqqb2Xr1UHW5GsbkqpIa2Hv1sR51+tqNOZlLF7DytKw9fO7Vi3gylsPYUkJDiGaDGOmd9ptNdd0n6uXNu0LOtzDl3zMwWSnrezColtY3x3DFf08w+KemTklRWVhZ71QAAABegKCdNH7hqjj5w1RwNDTnta2gPd/eratK2Aye0q+6kdtWd1LdeqFZWakDXLgrqxiXhdunzg1l+lw8gRvEMUHWS5noez5F07Az73iXpM94NzrljkT9rzez3kq6S9LSkfDNLjpyFOuNrOucekfSIFJ7Cd+FvAwAA4PwkJZmWzsrV0lm5+vSNi9TVN6CtB1q0eX+TtlSFVN3Yod++fVy/ffu4JKmsIHO4GcW1iwqVm57i8zsAcCbxvAYqWeEmEjdLOqpwE4mPOuf2jNrvEknPSVrgIsWY2QxJXc65XjMLSnpV0p3Oub1m9jNJT3uaSOxyzn3nbLVwDRQAAJhMjrZ266WqJm3eH9JL1SGd7O4fHgskmVaU5WtteZHWLSlSxew8BVh7Cog735tIRIpYL+kbCrcx/4Fz7u/M7GFJ251zz0T2+aqkdOfcQ57nXSfpXyQNSUqS9A3n3KORsYUaaWP+pqR7nHO9Z6uDAAUAACarwSGnXXWt2hJpRvHmkVYNehafys9M0fWLg1pXHp7uNys/w8dqgalrUgSoyYIABQAAEkVbT79ehgXfoQAAEINJREFUqW4Ot0uvatKRlu6o8cXF2VpbHtTV8wu0uDhb8wozlZbM+lPAeBGgPAhQAAAgETnndKi5S5sj0/1erQmps28wap8kC19DtagoW4uKs7WoKCt8vyhbM2idDsSMAOVBgAIAAFNB/+CQ3jh0QluqQtpb36aapg4daenS0Bm+zhVkpQ4HqoWeYDVnRoaSaaMORJkM60ABAABgAqUEkrRmYaHWLCwc3tbTP6hDzV2qaepQTWOHakOdw/dbOvvU0tmnbQdPRL1OaiBJ84OZw4FqUfGpkJWt7DS+HgJnw/8hAAAACSw9JaBLSnJ0SUlO1HbnnI639YbDVCRQ1TSFw1X9yR7tP96h/cc7Tnu9ktz04UDlDVgluekyoxsgQIACAACYgsxMJXnpKslL1/WLg1FjHb0DOhAJU6dutU2dqg11qqGtRw1tPXq5ujnqOZmpgahpgKeC1fzCLKWn0MQC0wcBCgAAYJrJTktWxZw8VczJi9o+OOR09ER3VLCqaQwHrebOPu0+2qbdR9uinmMmzZ2ROdK8ovhUwMpSQVYqZ60w5dBEAgAAAOd0orNPtaGRQBW+depwS1fUulVe+ZkpWhg8PViVFWTSxAKTDk0kAAAAMGFmZKVqZVaBVs4riNreNzCkwy2dqh4VrGobO9Ta1a83DrfqjcOtUc9JCZjmFWZFtVxfVBzuFJibnnIx3xZw3ghQAAAAuGCpyUlaXJyjxcWnN7Foau9VdSRQhZtYhK+1OtrarerGDlU3dkg6HvW84py04eurFgZH1raalZehpCSmA8J/BCgAAABMODNTcW66inPTdd2i6CYWXX0Dw00rTgWrmqZO1TZ1qLG9V43tvXq1NrqJRXpKUlSgOnXmamERTSxwcRGgAAAAcFFlpiZr+ew8LZ8d3cRiaMjpaGv3cKDytl8PdfRqb32b9taf3sRidn7GaWtaLSrKVjCbJhaYeDSRAAAAwKR3sqtfNaHo9axqmzp0qLlLA2doYpGTnjxmsJpXmKkUmlhgFJpIAAAAYMrIy0zRirIZWlE2I2p7/+CQDrd0RQWrmqbw9VXtPQPaeaRVO49EN7FITjKVFWZ61rTKCk8NDGYrL5MmFjg7AhQAAAASVkogaTgIeTnnFOroO209q9pQh+pOdIevwWrq1G9GNbEIZqdq4ahgtbgoW7PyMxSgiQVEgAIAAMAUZGYqyklTUU6arllYGDXW0z+oA6HOqGB1qkNgqKNPoY4WvX6gJeo5aclJWhDMilrP6lQTi8xUvlJPJ/zXBgAAwLSSnhLQZaW5uqw0N2r70JBTfVuPpzPgSMBqbO/VvoZ27WtoP+31ZudnaOFwZ8CRhYOLc9JoYjEF0UQCAAAAOIe2nn4d8FxjdSpYHWzuVP/g2N+ns9OSowLVqfvzCrOUmkwTi8mGJhIAAADABMlNT9EVc/N1xdz8qO0Dg0M6cqI7+qxVU6eqGzt0srtfb9Wd1Ft1J6OeE0gylRVknrae1aKibM3ISr2YbwsXgDNQAAAAwARzzqmls294gWDv2lZHWrp0hs7rKshKjQpWp9qvz5mRSROLOOMMFAAAAOATM1NhdpoKs9O0ekFB1FhP/6AONXd5FgoeCVctnX1q6ezTtoMnop6TGjjVxMITriJnrrLS+Ep/MXG0AQAAgIsoPSWgS0pydElJTtR255wa2npO6wxY09Sh+pM9eud4u945fnoTi5Lc9NOC1aLiLJXkptPEIg4IUAAAAMAkYGYqzctQaV6GbigPRo119A5EN7GINLI4EOpUQ1uPGtp69HJ1c9RzslIDkTWtvI0ssjWvMFPpKYGL+damFAIUAAAAMMllpyWrYk6eKubkRW0fHHKqO9E15ppWzZ19qjx6UpVHo5tYJJk0tyAzPAVw1NpWBVmpnLU6BwIUAAAAkKACSaZ5hVmaV5ild18aPXais0+1oehgVdPUqUPNnTrU3KVDzV16ftTr5WemRK9nFTlzNXdGhpIDtF6XCFAAAADAlDQjK1Urswq0cl50E4vegUEdPtXEoqkzqpFFa1e/dhw6oR2HoptYpARM8wuzhq+vWhgMB6uFRVnKTU+5mG/LdwQoAAAAYBpJSw6ofGaOymee3sSiqb1X1aOCVW1Tp462dquqsUNVjR3SnujXK85Ji2q5fuqsVWluupKmYOt1AhQAAAAAmZmKc9NVnJuu6xZFN7Ho6hsY7gh4quV6TWOHDoQ61djeq8b2Xr1aG93EIiMlMLxAsDdgLQhmJXQTCwIUAAAAgLPKTE3W8tl5Wj779CYWx1q7Ve1puR4+c9WpUEev9hxr055jbVHPMZNm52doUVG27rxylv5wxZyL+VbGjQAFAAAA4IIEkkxzCzI1tyBTN10SPXayq181oZFAdaqRxaHmLtWd6FbdiW5dVZbvT+HjQIACAAAAMOHyMlO0omyGVpTNiNreNzCkwy3hJhYLg1k+VXfhCFAAAAAALprU5CQtLs7W4uJsv0u5IDRzBwAAAIAYEaAAAAAAIEYEKAAAAACIEQEKAAAAAGJEgAIAAACAGBGgAAAAACBGBCgAAAAAiBEBCgAAAABiRIACAAAAgBgRoAAAAAAgRgQoAAAAAIgRAQoAAAAAYkSAAgAAAIAYEaAAAAAAIEYEKAAAAACIUVwDlJndZmbvmFm1mT00xvjXzWxn5LbfzFoj2680s1fNbI+Z7TKzj3ies8HMDnied2U83wMAAAAAnJIcrxc2s4Ckb0u6RVKdpG1m9oxzbu+pfZxzX/Ds/zlJV0Uedkn6mHOuysxmSdphZs8551oj43/hnPt5vGoHAAAAgLHE8wzUaknVzrla51yfpKck3XmW/e+W9BNJcs7td85VRe4fk9QoqSiOtQIAAADAOcUzQM2WdMTzuC6y7TRmNk/SAknPjzG2WlKqpBrP5r+LTO37upmlTVzJAAAAAHBmcZvCJ8nG2ObOsO9dkn7unBuMegGzUklPSLrPOTcU2fwlSQ0Kh6pHJP2lpIdP+8vNPinpk5GHHWb2znm/g/gISgr5XcQUxzGOP45x/HGM44vjG38c4/jjGMcfxzj+JtMxnhfLTvEMUHWS5noez5F07Az73iXpM94NZpYr6VlJf+2ce+3UdudcfeRur5k9JumLY72gc+4RhQPWpGJm251zq/yuYyrjGMcfxzj+OMbxxfGNP45x/HGM449jHH+JeIzjOYVvm6RyM1tgZqkKh6RnRu9kZpdImiHpVc+2VEm/kPS4c+5no/Yvjfxpkv5A0u64vQMAAAAA8IjbGSjn3ICZfVbSc5ICkn7gnNtjZg9L2u6cOxWm7pb0lHPOO73vw5LWSSo0s/sj2+53zu2U9CMzK1J4iuBOSZ+O13sAAAAAAK94TuGTc26jpI2jtn1l1OOvjvG8JyU9eYbXfPcEluiHSTetcAriGMcfxzj+OMbxxfGNP45x/HGM449jHH8Jd4wt+sQPAAAAAOBM4nkNFAAAAABMKQSoODGz28zsHTOrNrOHxhhPM7N/jYxvNbP5F7/KxBbDMb7fzJrMbGfk9gk/6kxUZvYDM2s0szEbtVjYNyPHf5eZrbjYNSa6GI7xu8zspOcz/JWx9sPYzGyumb1gZm+b2R4z+7Mx9uFzPA4xHmM+x+NgZulm9rqZvRU5xn87xj58pxiHGI8x3ynGycwCZvammf1qjLGE+gzH9Rqo6crMApK+LekWhdu5bzOzZ5xzez27PSjphHNusZndJenvJX3k4lebmGI8xpL0r865z170AqeGDZK+JenxM4zfLqk8clsj6Z8jfyJ2G3T2YyxJW5xz77s45Uw5A5L+3Dn3hpnlSNphZr8Z9e8En+PxieUYS3yOx6NX0rudcx1mliLpJTPb5F3iRXynGK9YjrHEd4rx+jNJb0vKHWMsoT7DnIGKj9WSqp1ztc65PklPSbpz1D53Svph5P7PJd0cac2O2MRyjDEOzrnNklrOssudCi814CI/ZPJPLTOA2MRwjDEOzrl659wbkfvtCv/gnj1qNz7H4xDjMcY4RD6bHZGHKZHb6AvY+U4xDjEeY4yDmc2RdIek759hl4T6DBOg4mO2pCOex3U6/QfK8D7OuQFJJyUVXpTqpoZYjrEkfTAyLefnZjZ3jHFcuFj/G2B8ro1MK9lkZsv8LiZRRaaDXCVp66ghPscT5CzHWOJzPC6RqU87JTVK+o1z7oyfY75TXJgYjrHEd4rx+Iak/y5p6AzjCfUZJkDFx1iJefRvMmLZB2cWy/H7D0nznXOXS/qtRn6zgYnBZzj+3pA0zzl3haT/X9Ivfa4nIZlZtqSnJf0351zb6OExnsLn+Dyd4xjzOR4n59ygc+5KSXMkrTaz5aN24XM8TjEcY75TXCAze5+kRufcjrPtNsa2SfsZJkDFR50k728m5kg6dqZ9zCxZUp6YynM+znmMnXPNzrneyMPvSVp5kWqbLmL5nGMcnHNtp6aVRNbVSzGzoM9lJZTI9QxPS/qRc+7fxtiFz/E4nesY8zmeOM65Vkm/l3TbqCG+U0yQMx1jvlOMy/WS3m9mBxW+5OLdZjZ6vdeE+gwToOJjm6RyM1tgZqmS7pL0zKh9npF0X+T+hyQ971iU63yc8xiPuo7h/QrPzcfEeUbSxyJdzK6RdNI5V+93UVOJmZWcmgNuZqsV/je72d+qEkfk2D0q6W3n3D+eYTc+x+MQyzHmczw+ZlZkZvmR+xmS3iNp36jd+E4xDrEcY75TXDjn3Jecc3Occ/MV/r72vHPunlG7JdRnmC58ceCcGzCzz0p6TlJA0g+cc3vM7GFJ251zzyj8A+cJM6tWOGHf5V/FiSfGY/x5M3u/wl2iWiTd71vBCcjMfiLpXZKCZlYn6W8UvrBWzrnvStooab2kakldkj7uT6WJK4Zj/CFJf2pmA5K6Jd01mX+gTELXS7pXUmXk2gZJ+h+SyiQ+xxMklmPM53h8SiX9MNJ9NknST51zv+I7xYSK5RjznWKCJfJn2Pg3DAAAAABiwxQ+AAAAAIgRAQoAAAAAYkSAAgAAAIAYEaAAAAAAIEYEKAAAAACIEQEKAJCwzGzQzHZ6bg9N4GvPN7PdE/V6AICpgXWgAACJrNs5d6XfRQAApg/OQAEAphwzO2hmf29mr0duiyPb55nZ78xsV+TPssj2mWb2CzN7K3K7LvJSATP7npntMbNfm1mGb28KADApEKAAAIksY9QUvo94xtqcc6slfUvSNyLbviXpcefc5ZJ+JOmbke3flPSic+4KSSsk7YlsL5f0befcMkmtkj4Y5/cDAJjkzDnndw0AAFwQM+twzmWPsf2gpHc752rNLEVSg3Ou0MxCkkqdc/2R7fXOuaCZNUma45zr9bzGfEm/cc6VRx7/paQU59z/iv87AwBMVpyBAgBMVe4M98+0z1h6PfcHxbXDADDtEaAAAFPVRzx/vhq5/4qkuyL3/1jSS5H7v5P0p5JkZgEzy71YRQIAEgu/SQMAJLIMM9vpefyfzrlTrczTzGyrwr8svDuy7fOSfmBmfyGpSdLHI9v/TNIjZvagwmea/lRSfdyrBwAkHK6BAgBMOZFroFY550J+1wIAmFqYwgcAAAAAMeIMFAAAAADEiDNQAAAAABAjAhQAAAAAxIgABQAAAAAxIkABAAAAQIwIUAAAAAAQIwIUAAAAAMTo/wJrHeIf6NzBuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'], linewidth=2, label='Train')\n",
    "plt.plot(history['val_loss'], linewidth=2, label='Test')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "#plt.ylim(ymin=0.70,ymax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reconstruction_error</th>\n",
       "      <th>True_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16342.000000</td>\n",
       "      <td>16342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.828304</td>\n",
       "      <td>0.002570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.921153</td>\n",
       "      <td>0.050632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.049948</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.240817</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.391675</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.630130</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>169.032354</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Reconstruction_error    True_class\n",
       "count          16342.000000  16342.000000\n",
       "mean               0.828304      0.002570\n",
       "std                3.921153      0.050632\n",
       "min                0.049948      0.000000\n",
       "25%                0.240817      0.000000\n",
       "50%                0.391675      0.000000\n",
       "75%                0.630130      0.000000\n",
       "max              169.032354      1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x_predictions = autoencoder.predict(test_x)\n",
    "mse = np.mean(np.power(test_x - test_x_predictions, 2), axis=1)\n",
    "error_df = pd.DataFrame({'Reconstruction_error': mse,\n",
    "                        'True_class': test_y})\n",
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_pos_rate, true_pos_rate, thresholds = roc_curve(error_df.True_class, error_df.Reconstruction_error)\n",
    "roc_auc = auc(false_pos_rate, true_pos_rate,)\n",
    "\n",
    "plt.plot(false_pos_rate, true_pos_rate, linewidth=5, label='AUC = %0.3f'% roc_auc)\n",
    "plt.plot([0,1],[0,1], linewidth=5)\n",
    "\n",
    "plt.xlim([-0.01, 1])\n",
    "plt.ylim([0, 1.01])\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Receiver operating characteristic curve (ROC)')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision_rt, recall_rt, threshold_rt = precision_recall_curve(error_df.True_class, error_df.Reconstruction_error)\n",
    "plt.plot(recall_rt, precision_rt, linewidth=5, label='Precision-Recall curve')\n",
    "plt.title('Recall vs Precision')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(threshold_rt, precision_rt[1:], label=\"Precision\",linewidth=5)\n",
    "plt.plot(threshold_rt, recall_rt[1:], label=\"Recall\",linewidth=5)\n",
    "plt.title('Precision and recall for different threshold values')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision/Recall')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_fixed = 5\n",
    "groups = error_df.groupby('True_class')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.index, group.Reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
    "            label= \"Fraud\" if name == 1 else \"Normal\")\n",
    "ax.hlines(threshold_fixed, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
    "ax.legend()\n",
    "plt.title(\"Reconstruction error for different classes\")\n",
    "plt.ylabel(\"Reconstruction error\")\n",
    "plt.xlabel(\"Data point index\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_y = [1 if e > threshold_fixed else 0 for e in error_df.Reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.True_class, pred_y)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
